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DArTseq-Based High-Throughput SilicoDArT and SNP Markers Applied for Association Mapping of Genes Related to Maize Morphology. Int J Mol Sci 2021; 22:ijms22115840. [PMID: 34072515 PMCID: PMC8198497 DOI: 10.3390/ijms22115840] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/23/2021] [Accepted: 05/26/2021] [Indexed: 01/30/2023] Open
Abstract
Today, agricultural productivity is essential to meet the needs of a growing population, and is also a key tool in coping with climate change. Innovative plant breeding technologies such as molecular markers, phenotyping, genotyping, the CRISPR/Cas method and next-generation sequencing can help agriculture meet the challenges of the 21st century more effectively. Therefore, the aim of the research was to identify single-nucleotide polymorphisms (SNPs) and SilicoDArT markers related to select morphological features determining the yield in maize. The plant material consisted of ninety-four inbred lines of maize of various origins. These lines were phenotyped under field conditions. A total of 14 morphological features was analyzed. The DArTseq method was chosen for genotyping because this technique reduces the complexity of the genome by restriction enzyme digestion. Subsequently, short fragment sequencing was used. The choice of a combination of restrictases allowed the isolation of highly informative low copy fragments of the genome. Thanks to this method, 90% of the obtained DArTseq markers are complementary to the unique sequences of the genome. All the observed features were normally distributed. Analysis of variance indicated that the main effect of lines was statistically significant (p < 0.001) for all 14 traits of study. Thanks to the DArTseq analysis with the use of next-generation sequencing (NGS) in the studied plant material, it was possible to identify 49,911 polymorphisms, of which 33,452 are SilicoDArT markers and the remaining 16,459 are SNP markers. Among those mentioned, two markers associated with four analyzed traits deserved special attention: SNP (4578734) and SilicoDArT (4778900). SNP marker 4578734 was associated with the following features: anthocyanin coloration of cob glumes, number of days from sowing to anthesis, number of days from sowing to silk emergence and anthocyanin coloration of internodes. SilicoDArT marker 4778900 was associated with the following features: number of days from sowing to anthesis, number of days from sowing to silk emergence, tassel: angle between the axis and lateral branches and plant height. Sequences with a length of 71 bp were used for physical mapping. The BLAST and EnsemblPlants databases were searched against the maize genome to identify the positions of both markers. Marker 4578734 was localized on chromosome 7, the closest gene was Zm00001d022467, approximately 55 Kb apart, encoding anthocyanidin 3-O-glucosyltransferase. Marker 4778900 was located on chromosome 7, at a distance of 45 Kb from the gene Zm00001d045261 encoding starch synthase I. The latter observation indicated that these flanking SilicoDArT and SNP markers were not in a state of linkage disequilibrium.
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Puglisi D, Delbono S, Visioni A, Ozkan H, Kara İ, Casas AM, Igartua E, Valè G, Piero ARL, Cattivelli L, Tondelli A, Fricano A. Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction. FRONTIERS IN PLANT SCIENCE 2021; 12:664148. [PMID: 34108982 PMCID: PMC8183822 DOI: 10.3389/fpls.2021.664148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/26/2021] [Indexed: 06/12/2023]
Abstract
Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.
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Affiliation(s)
- Damiano Puglisi
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Stefano Delbono
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Andrea Visioni
- Biodiversity and Crop Improvement Program, International Center for Agricultural Research in the Dry Areas, Avenue Hafiane Cherkaoui, Rabat, Morocco
| | - Hakan Ozkan
- Department of Field Crops, Faculty of Agriculture, University of Cukurova, Adana, Turkey
| | - İbrahim Kara
- Bahri Dagdas International Agricultural Research Institute, Konya, Turkey
| | - Ana M. Casas
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Ernesto Igartua
- Aula Dei Experimental Station (EEAD-CSIC), Spanish Research Council, Zaragoza, Spain
| | - Giampiero Valè
- DiSIT, Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale, Vercelli, Italy
| | - Angela Roberta Lo Piero
- Dipartimento di Agricoltura, Alimentazione e Ambiente (Di3A), Università di Catania, Catania, Italy
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Alessandro Tondelli
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Agostino Fricano
- Council for Agricultural Research and Economics–Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
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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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Nagamatsu S, Tsubone M, Wada T, Oku K, Mori M, Hirata C, Hayashi A, Tanabata T, Isobe S, Takata K, Shimomura K. Strawberry fruit shape: quantification by image analysis and QTL detection by genome-wide association analysis. BREEDING SCIENCE 2021; 71:167-175. [PMID: 34377064 PMCID: PMC8329875 DOI: 10.1270/jsbbs.19106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/12/2020] [Indexed: 06/13/2023]
Abstract
Fruit shape of cultivated strawberry (Fragaria × ananassa Duch.) is an important breeding target. To detect genomic regions associated with this trait, its quantitative evaluation is needed. Previously we created a multi-parent advanced-generation inter-cross (MAGIC) strawberry population derived from six founder parents. In this study, we used this population to quantify fruit shape. Elliptic Fourier descriptors (EFDs) were generated from 2 969 two-dimensional binarized fruit images, and principal component (PC) scores were calculated on the basis of the EFD coefficients. PC1-PC3 explained 96% of variation in shape and thus adequately quantified it. In genome-wide association study, the PC scores were used as phenotypes. Genome wide association study using mixed linear models revealed 2 quantitative trait loci (QTLs) for fruit shape. Our results provide a novel and effective method to analyze strawberry fruit morphology; the detected QTLs and presented method can support marker-assisted selection in practical breeding programs to improve fruit shape.
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Affiliation(s)
- Shiro Nagamatsu
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Masao Tsubone
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Takuya Wada
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Koichiro Oku
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Miyuki Mori
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Chiharu Hirata
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Atsushi Hayashi
- Department of Frontier Research, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Takanari Tanabata
- Department of Frontier Research, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Sachiko Isobe
- Department of Frontier Research, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
| | - Kinuko Takata
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
| | - Katsumi Shimomura
- Fukuoka Agriculture and Forestry Research Center, 587 Yoshiki, Chikushino, Fukuoka 818-8549, Japan
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Braz GT, Yu F, Zhao H, Deng Z, Birchler JA, Jiang J. Preferential meiotic chromosome pairing among homologous chromosomes with cryptic sequence variation in tetraploid maize. THE NEW PHYTOLOGIST 2021; 229:3294-3302. [PMID: 33222183 DOI: 10.1111/nph.17098] [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: 09/06/2020] [Accepted: 11/13/2020] [Indexed: 06/11/2023]
Abstract
Meiotic chromosome pairing between homoeologous chromosomes was reported in many nascent allopolyploids. Homoeologous pairing is gradually eliminated and replaced by exclusive homologous pairing in well-established allopolyploids, an evolutionary process referred to as the diploidization of allopolyploids. A fundamental question of the diploidization of allopolyploids is whether and to what extent the DNA sequence variation among homoeologous chromosomes contribute to the establishment of exclusive homologous chromosome pairing. We developed aneuploid tetraploid maize lines that contain three copies of chromosome 10 derived from inbred lines B73 and H99. We were able to identify the parental origin of each copy of chromosome 10 in the materials using oligonucleotide-based haplotype-specific chromosome painting. We demonstrate that the two identical copies of chromosome 10 from H99 pair preferentially over chromosome 10 from B73 in different stages of prophase I and metaphase I during meiosis. Thus, homologous chromosome pairing is favored to partners with the most similar DNA sequences and can be discriminated based on cryptic sequence variation. We propose that innate preference of homologous chromosome pairing exists in nascent allopolyploids and serves as the first layer that would eventually block all homoeologous chromosome pairing in allopolyploids.
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Affiliation(s)
- Guilherme T Braz
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Fan Yu
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
- National Engineering Research Center for Sugarcane, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Hainan Zhao
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Zuhu Deng
- National Engineering Research Center for Sugarcane, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - James A Birchler
- Division of Biological Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Jiming Jiang
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Horticulture, Michigan State University, East Lansing, MI, 48824, USA
- Michigan State University AgBioResearch, East Lansing, MI, 48824, USA
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Zhang Y, Wang J, Du J, Zhao Y, Lu X, Wen W, Gu S, Fan J, Wang C, Wu S, Wang Y, Liao S, Zhao C, Guo X. Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:35-50. [PMID: 32569428 PMCID: PMC7769239 DOI: 10.1111/pbi.13437] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 06/03/2020] [Accepted: 06/15/2020] [Indexed: 05/27/2023]
Abstract
High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high-throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Jinglu Wang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Jianjun Du
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular BreedingMaize Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Xianju Lu
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Weiliang Wen
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Shenghao Gu
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Jiangchuan Fan
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Chuanyu Wang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Sheng Wu
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Yongjian Wang
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Shengjin Liao
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Chunjiang Zhao
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
| | - Xinyu Guo
- Beijing Key Lab of Digital PlantNational Engineering Research Center for Information Technology in AgricultureBeijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingChina
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Rollar S, Serfling A, Geyer M, Hartl L, Mohler V, Ordon F. QTL mapping of adult plant and seedling resistance to leaf rust (Puccinia triticina Eriks.) in a multiparent advanced generation intercross (MAGIC) wheat population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:37-51. [PMID: 33201290 PMCID: PMC7813716 DOI: 10.1007/s00122-020-03657-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 07/28/2020] [Indexed: 05/22/2023]
Abstract
The Bavarian MAGIC Wheat population, comprising 394 F6:8 recombinant inbred lines was phenotyped for Puccinia triticina resistance in multi-years' field trials at three locations and in a controlled environment seedling test. Simple intervall mapping revealed 19 QTL, corresponding to 11 distinct chromosomal regions. The biotrophic rust fungus Puccinia triticina is one of the most important wheat pathogens with the potential to cause yield losses up to 70%. Growing resistant cultivars is the most cost-effective and environmentally friendly way to encounter this problem. The emergence of leaf rust races being virulent against common resistance genes increases the demand for wheat varieties with novel resistances. In the past decade, the use of complex experimental populations, like multiparent advanced generation intercross (MAGIC) populations, has risen and offers great advantages for mapping resistances. The genetic diversity of multiple parents, which has been recombined over several generations, leads to a broad phenotypic diversity, suitable for high-resolution mapping of quantitative traits. In this study, interval mapping was performed to map quantitative trait loci (QTL) for leaf rust resistance in the Bavarian MAGIC Wheat population, comprising 394 F6:8 recombinant inbred lines (RILs). Phenotypic evaluation of the RILs for adult plant resistance was carried out in field trials at three locations and two years, as well as in a controlled-environment seedling inoculation test. In total, interval mapping revealed 19 QTL, which corresponded to 11 distinct chromosomal regions controlling leaf rust resistance. Six of these regions may represent putative new QTL. Due to the elite parental material, RILs identified to be resistant to leaf rust can be easily introduced in breeding programs.
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Affiliation(s)
- Sandra Rollar
- Institute for Resistance Research and Stress Tolerance, Julius Kuehn-Institute, Erwin Baur‑Straße 27, 06484 Quedlinburg, Germany
| | - Albrecht Serfling
- Institute for Resistance Research and Stress Tolerance, Julius Kuehn-Institute, Erwin Baur‑Straße 27, 06484 Quedlinburg, Germany
| | - Manuel Geyer
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Am Gereuth 8, Freising, Germany
| | - Lorenz Hartl
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Am Gereuth 8, Freising, Germany
| | - Volker Mohler
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Am Gereuth 8, Freising, Germany
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kuehn-Institute, Erwin Baur‑Straße 27, 06484 Quedlinburg, Germany
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Li Y, Yang C, Ahmad H, Maher M, Fang C, Luo J. Benefiting others and self: Production of vitamins in plants. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2021; 63:210-227. [PMID: 33289302 DOI: 10.1111/jipb.13047] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 11/26/2020] [Indexed: 06/12/2023]
Abstract
Vitamins maintain growth and development in humans, animals, and plants. Because plants serve as essential producers of vitamins, increasing the vitamin contents in plants has become a goal of crop breeding worldwide. Here, we begin with a summary of the functions of vitamins. We then review the achievements to date in elucidating the molecular mechanisms underlying how vitamins are synthesized, transported, and regulated in plants. We also stress the exploration of variation in vitamins by the use of forward genetic approaches, such as quantitative trait locus mapping and genome-wide association studies. Overall, we conclude that exploring the diversity of vitamins could provide new insights into plant metabolism and crop breeding.
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Affiliation(s)
- Yufei Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Chenkun Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Hasan Ahmad
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Mohamed Maher
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Chuanying Fang
- College of Tropical Crops, Hainan University, Haikou, 570228, China
| | - Jie Luo
- College of Tropical Crops, Hainan University, Haikou, 570228, China
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Ayaad M, Han Z, Zheng K, Hu G, Abo-Yousef M, Sobeih SES, Xing Y. Bin-based genome-wide association studies reveal superior alleles for improvement of appearance quality using a 4-way MAGIC population in rice. J Adv Res 2020; 28:183-194. [PMID: 33364055 PMCID: PMC7753235 DOI: 10.1016/j.jare.2020.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/19/2020] [Accepted: 08/02/2020] [Indexed: 12/18/2022] Open
Abstract
4-way Multiparental population covered the limitations of the biparental structure. The combination of SNP and bin-GWAS showed a powerful tool for QTL mapping. qPGWC8.2 harbored a novel predicted gene for rice chalkiness quality.
Introduction The multiparental population provides us the chance to identify superior alleles controlling a trait for genetic improvement. Genome wide association studies at bin level (bin-GWAS) are expected to be more power in QTL mapping than GWAS at SNP level (SNP-GWAS). Objectives This study is to estimate genetic effects of QTL conferring grain appearance quality in rice by SNP-GWAS and bin-GWAS, compare their power in QTL mapping and identify the superior alleles of all detected QTL from 4 parents for genetic improvement. Methods A 4-way MAGIC population and its four founders were cultivated in two environments to dissect the genetic basis of rice grain appearance quality. Both SNP-GWAS and bin-GWAS were conducted for QTL mapping. Multiple comparison among 4 parental bin/alleles was used to identify the superior alleles. Results A total of 16 and 20 QTL associated with grain appearance quality were identified by SNP- and bin-GWAS, respectively. A minor chalkiness QTL qPGWC8.2/qDEC8 was assigned to a 30-kb genomic region, in which OsMH_08T0121900 is the potential candidate gene because its encoded protein, glucan endo-1,3-beta-glucosidase precursor is involved in the starch and sucrose metabolism pathway. The superior parental alleles for GS3, GL3.1, GW5, GW7, and Chalk5 and two QTLs were almost carried by the high-quality parents Cypress and Yuejingsimiao (YJSM), while the poor-quality parent Guichao-2 (GC2) always carried the inferior alleles. The top five recombinant inbred lines with the highest quality of grain shape and chalkiness traits all carried gene combinations of superior alleles. Conclusions Both SNP- and bin-GWAS methods are encouraged for joint QTL mapping with MAGIC population. qPGWC8.2/qDEC8 is a novel candidate gene strongly associated with chalkiness. The superior alleles of GS3, GW5, GL3.1, GW7, Chalk5 and qPGWC8.2 were identified, and the pyramiding of these superior alleles is helpful to improve rice appearance quality.
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Affiliation(s)
- Mohammed Ayaad
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan 430070, China.,Plant Research Department, Nuclear Research Center, Atomic Energy Authority, Abo-Zaabal 13759, Egypt
| | - Zhongmin Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan 430070, China
| | - Kou Zheng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan 430070, China
| | - Gang Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan 430070, China
| | - Mahmoud Abo-Yousef
- Rice Research and Training Center, Agriculture Research Center, Sakha 33717, Egypt
| | - Sobeih El S Sobeih
- Plant Research Department, Nuclear Research Center, Atomic Energy Authority, Abo-Zaabal 13759, Egypt
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan 430070, China
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Novakazi F, Krusell L, Jensen JD, Orabi J, Jahoor A, Bengtsson T. You Had Me at "MAGIC"!: Four Barley MAGIC Populations Reveal Novel Resistance QTL for Powdery Mildew. Genes (Basel) 2020; 11:genes11121512. [PMID: 33352820 PMCID: PMC7766815 DOI: 10.3390/genes11121512] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/11/2020] [Accepted: 12/15/2020] [Indexed: 11/23/2022] Open
Abstract
Blumeria graminis f. sp. hordei (Bgh), the causal agent of barley powdery mildew (PM), is one of the most important barley leaf diseases and is prevalent in most barley growing regions. Infection decreases grain quality and yields on average by 30%. Multi-parent advanced generation inter-cross (MAGIC) populations combine the advantages of bi-parental and association panels and offer the opportunity to incorporate exotic alleles into adapted material. Here, four barley MAGIC populations consisting of six to eight founders were tested for PM resistance in field trials in Denmark. Principle component and STRUCTURE analysis showed the populations were unstructured and genome-wide linkage disequilibrium (LD) decay varied between 14 and 38 Mbp. Genome-wide association studies (GWAS) identified 11 regions associated with PM resistance located on chromosomes 1H, 2H, 3H, 4H, 5H and 7H, of which three regions are putatively novel resistance quantitative trait locus/loci (QTL). For all regions high-confidence candidate genes were identified that are predicted to be involved in pathogen defense. Haplotype analysis of the significant SNPs revealed new allele combinations not present in the founders and associated with high resistance levels.
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Affiliation(s)
- Fluturë Novakazi
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 101, 23053 Alnarp, Sweden; (F.N.); (A.J.)
| | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700 Horsens, Denmark;
| | - Jens Due Jensen
- Nordic Seed A/S, Kornmarken 1, 8464 Galten, Denmark; (J.D.J.); (J.O.)
| | - Jihad Orabi
- Nordic Seed A/S, Kornmarken 1, 8464 Galten, Denmark; (J.D.J.); (J.O.)
| | - Ahmed Jahoor
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 101, 23053 Alnarp, Sweden; (F.N.); (A.J.)
- Nordic Seed A/S, Kornmarken 1, 8464 Galten, Denmark; (J.D.J.); (J.O.)
| | - Therése Bengtsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, P.O. Box 101, 23053 Alnarp, Sweden; (F.N.); (A.J.)
- Correspondence:
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Scott MF, Ladejobi O, Amer S, Bentley AR, Biernaskie J, Boden SA, Clark M, Dell'Acqua M, Dixon LE, Filippi CV, Fradgley N, Gardner KA, Mackay IJ, O'Sullivan D, Percival-Alwyn L, Roorkiwal M, Singh RK, Thudi M, Varshney RK, Venturini L, Whan A, Cockram J, Mott R. Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity (Edinb) 2020; 125:396-416. [PMID: 32616877 PMCID: PMC7784848 DOI: 10.1038/s41437-020-0336-6] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.
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Affiliation(s)
| | | | - Samer Amer
- University of Reading, Reading, RG6 6AH, UK
- Faculty of Agriculture, Alexandria University, Alexandria, 23714, Egypt
| | - Alison R Bentley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Jay Biernaskie
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
| | - Scott A Boden
- School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia
| | | | | | - Laura E Dixon
- Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK
| | - Carla V Filippi
- Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Nicolas Repetto y Los Reseros s/n, 1686, Hurlingham, Buenos Aires, Argentina
| | - Nick Fradgley
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Keith A Gardner
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Ian J Mackay
- SRUC, West Mains Road, Kings Buildings, Edinburgh, EH9 3JG, UK
| | | | | | - Manish Roorkiwal
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rakesh Kumar Singh
- International Center for Biosaline Agriculture, Academic City, Dubai, United Arab Emirates
| | - Mahendar Thudi
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Rajeev Kumar Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | | | - Alex Whan
- CSIRO, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - James Cockram
- The John Bingham Laboratory, NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Richard Mott
- UCL Genetics Institute, Gower Street, London, WC1E 6BT, UK
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Bohra A, Chand Jha U, Godwin ID, Kumar Varshney R. Genomic interventions for sustainable agriculture. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:2388-2405. [PMID: 32875704 PMCID: PMC7680532 DOI: 10.1111/pbi.13472] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/21/2020] [Accepted: 08/16/2020] [Indexed: 05/05/2023]
Abstract
Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.
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Affiliation(s)
- Abhishek Bohra
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Uday Chand Jha
- ICAR‐Indian Institute of Pulses Research (IIPR)KanpurIndia
| | - Ian D. Godwin
- Centre for Crop ScienceQueensland Alliance for Agriculture and Food Innovation (QAAFI)The University of QueenslandBrisbaneQldAustralia
| | - Rajeev Kumar Varshney
- International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- The UWA Institute of AgricultureThe University of Western AustraliaPerthAustralia
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Diaz S, Ariza-Suarez D, Izquierdo P, Lobaton JD, de la Hoz JF, Acevedo F, Duitama J, Guerrero AF, Cajiao C, Mayor V, Beebe SE, Raatz B. Genetic mapping for agronomic traits in a MAGIC population of common bean (Phaseolus vulgaris L.) under drought conditions. BMC Genomics 2020; 21:799. [PMID: 33198642 PMCID: PMC7670608 DOI: 10.1186/s12864-020-07213-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 11/05/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Common bean is an important staple crop in the tropics of Africa, Asia and the Americas. Particularly smallholder farmers rely on bean as a source for calories, protein and micronutrients. Drought is a major production constraint for common bean, a situation that will be aggravated with current climate change scenarios. In this context, new tools designed to understand the genetic basis governing the phenotypic responses to abiotic stress are required to improve transfer of desirable traits into cultivated beans. RESULTS A multiparent advanced generation intercross (MAGIC) population of common bean was generated from eight Mesoamerican breeding lines representing the phenotypic and genotypic diversity of the CIAT Mesoamerican breeding program. This population was assessed under drought conditions in two field trials for yield, 100 seed weight, iron and zinc accumulation, phenology and pod harvest index. Transgressive segregation was observed for most of these traits. Yield was positively correlated with yield components and pod harvest index (PHI), and negative correlations were found with phenology traits and micromineral contents. Founder haplotypes in the population were identified using Genotyping by Sequencing (GBS). No major population structure was observed in the population. Whole Genome Sequencing (WGS) data from the founder lines was used to impute genotyping data for GWAS. Genetic mapping was carried out with two methods, using association mapping with GWAS, and linkage mapping with haplotype-based interval screening. Thirteen high confidence QTL were identified using both methods and several QTL hotspots were found controlling multiple traits. A major QTL hotspot located on chromosome Pv01 for phenology traits and yield was identified. Further hotspots affecting several traits were observed on chromosomes Pv03 and Pv08. A major QTL for seed Fe content was contributed by MIB778, the founder line with highest micromineral accumulation. Based on imputed WGS data, candidate genes are reported for the identified major QTL, and sequence changes were identified that could cause the phenotypic variation. CONCLUSIONS This work demonstrates the importance of this common bean MAGIC population for genetic mapping of agronomic traits, to identify trait associations for molecular breeding tool design and as a new genetic resource for the bean research community.
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Affiliation(s)
- Santiago Diaz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Daniel Ariza-Suarez
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Paulo Izquierdo
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Present Address: Department of Plant Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
| | - Juan David Lobaton
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Present Address: School of Environmental and Rural Sciences, University of New England, Armidale, SA, Australia
| | - Juan Fernando de la Hoz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Present Address: Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Fernando Acevedo
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Departamento de Agronomía, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Jorge Duitama
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Present Address: Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Alberto F Guerrero
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Cesar Cajiao
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Victor Mayor
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Present Address: Progeny Breeding, Madrid, Colombia
| | - Stephen E Beebe
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Bodo Raatz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia.
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Kim KD, Kang Y, Kim C. Application of Genomic Big Data in Plant Breeding:Past, Present, and Future. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1454. [PMID: 33126607 PMCID: PMC7694055 DOI: 10.3390/plants9111454] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 01/11/2023]
Abstract
Plant breeding has a long history of developing new varieties that have ensured the food security of the human population. During this long journey together with humanity, plant breeders have successfully integrated the latest innovations in science and technologies to accelerate the increase in crop production and quality. For the past two decades, since the completion of human genome sequencing, genomic tools and sequencing technologies have advanced remarkably, and adopting these innovations has enabled us to cost down and/or speed up the plant breeding process. Currently, with the growing mass of genomic data and digitalized biological data, interdisciplinary approaches using new technologies could lead to a new paradigm of plant breeding. In this review, we summarize the overall history and advances of plant breeding, which have been aided by plant genomic research. We highlight the key advances in the field of plant genomics that have impacted plant breeding over the past decades and introduce the current status of innovative approaches such as genomic selection, which could overcome limitations of conventional breeding and enhance the rate of genetic gain.
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Affiliation(s)
- Kyung Do Kim
- Department of Bioscience and Bioinformatics, Myongji University, Yongin 17058, Korea;
| | - Yuna Kang
- Department of Crop Science, Chungnam National University, Daejeon 34134, Korea;
| | - Changsoo Kim
- Department of Crop Science, Chungnam National University, Daejeon 34134, Korea;
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon 34134, Korea
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Arrones A, Vilanova S, Plazas M, Mangino G, Pascual L, Díez MJ, Prohens J, Gramazio P. The Dawn of the Age of Multi-Parent MAGIC Populations in Plant Breeding: Novel Powerful Next-Generation Resources for Genetic Analysis and Selection of Recombinant Elite Material. BIOLOGY 2020; 9:biology9080229. [PMID: 32824319 PMCID: PMC7465826 DOI: 10.3390/biology9080229] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/13/2020] [Accepted: 08/13/2020] [Indexed: 12/15/2022]
Abstract
The compelling need to increase global agricultural production requires new breeding approaches that facilitate exploiting the diversity available in the plant genetic resources. Multi-parent advanced generation inter-cross (MAGIC) populations are large sets of recombinant inbred lines (RILs) that are a genetic mosaic of multiple founder parents. MAGIC populations display emerging features over experimental bi-parental and germplasm populations in combining significant levels of genetic recombination, a lack of genetic structure, and high genetic and phenotypic diversity. The development of MAGIC populations can be performed using “funnel” or “diallel” cross-designs, which are of great relevance choosing appropriate parents and defining optimal population sizes. Significant advances in specific software development are facilitating the genetic analysis of the complex genetic constitutions of MAGIC populations. Despite the complexity and the resources required in their development, due to their potential and interest for breeding, the number of MAGIC populations available and under development is continuously growing, with 45 MAGIC populations in different crops being reported here. Though cereals are by far the crop group where more MAGIC populations have been developed, MAGIC populations have also started to become available in other crop groups. The results obtained so far demonstrate that MAGIC populations are a very powerful tool for the dissection of complex traits, as well as a resource for the selection of recombinant elite breeding material and cultivars. In addition, some new MAGIC approaches that can make significant contributions to breeding, such as the development of inter-specific MAGIC populations, the development of MAGIC-like populations in crops where pure lines are not available, and the establishment of strategies for the straightforward incorporation of MAGIC materials in breeding pipelines, have barely been explored. The evidence that is already available indicates that MAGIC populations will play a major role in the coming years in allowing for impressive gains in plant breeding for developing new generations of dramatically improved cultivars.
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Affiliation(s)
- Andrea Arrones
- Instituto de Conservacióny Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera 14, 46022 Valencia, Spain; (A.A.); (M.P.); (G.M.); (M.J.D.); (J.P.)
| | - Santiago Vilanova
- Instituto de Conservacióny Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera 14, 46022 Valencia, Spain; (A.A.); (M.P.); (G.M.); (M.J.D.); (J.P.)
- Correspondence: (S.V.); (P.G.)
| | - Mariola Plazas
- Instituto de Conservacióny Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera 14, 46022 Valencia, Spain; (A.A.); (M.P.); (G.M.); (M.J.D.); (J.P.)
| | - Giulio Mangino
- Instituto de Conservacióny Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera 14, 46022 Valencia, Spain; (A.A.); (M.P.); (G.M.); (M.J.D.); (J.P.)
| | - Laura Pascual
- Department of Biotechnology-Plant Biology, School of Agricultural, Food and Biosystems Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
| | - María José Díez
- Instituto de Conservacióny Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera 14, 46022 Valencia, Spain; (A.A.); (M.P.); (G.M.); (M.J.D.); (J.P.)
| | - Jaime Prohens
- Instituto de Conservacióny Mejora de la Agrodiversidad Valenciana, Universitat Politècnica de València, Camino de Vera 14, 46022 Valencia, Spain; (A.A.); (M.P.); (G.M.); (M.J.D.); (J.P.)
| | - Pietro Gramazio
- Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
- Correspondence: (S.V.); (P.G.)
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Shi J, Wang J, Zhang L. Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations. J Hered 2020; 110:880-891. [PMID: 31419284 PMCID: PMC6916664 DOI: 10.1093/jhered/esz050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 08/12/2019] [Indexed: 12/17/2022] Open
Abstract
Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.
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Affiliation(s)
- Jinhui Shi
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jiankang Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Luyan Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Address correspondence to L. Zhang at the address above, or e-mail:
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Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I, Coppens F, Cornut G, Costa BV, Ćwiek-Kupczyńska H, Droesbeke B, Finkers R, Gruden K, Junker A, King GJ, Krajewski P, Lange M, Laporte MA, Michotey C, Oppermann M, Ostler R, Poorter H, Ramı Rez-Gonzalez R, Ramšak Ž, Reif JC, Rocca-Serra P, Sansone SA, Scholz U, Tardieu F, Uauy C, Usadel B, Visser RGF, Weise S, Kersey PJ, Miguel CM, Adam-Blondon AF, Pommier C. Enabling reusability of plant phenomic datasets with MIAPPE 1.1. THE NEW PHYTOLOGIST 2020. [PMID: 32171029 DOI: 10.15454/1yxvzv] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.
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Affiliation(s)
- Evangelia A Papoutsoglou
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ, the Netherlands
| | - Daniel Faria
- BioData.pt, Instituto Gulbenkian de Ciência, 2780-156, Oeiras, Portugal
- INESC-ID, 1000-029, Lisboa, Portugal
| | - Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Elizabeth Arnaud
- Bioversity International, Parc Scientifique Agropolis II, Montpellier Cedex 5, 34397, France
| | - Ioannis N Athanasiadis
- Geo-Information Science and Remote Sensing Laboratory, Wageningen University, Droevendaalsesteeg 3, Wageningen, 6708PB, the Netherlands
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, 2780-157, Oeiras, Portugal
- Instituto de Biologia Experimental e Tecnológica (iBET), 2780-157, Oeiras, Portugal
| | - Frederik Coppens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, 9052, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, Ghent, 9052, Belgium
| | | | - Bruno V Costa
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, 2780-157, Oeiras, Portugal
- BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal
| | - Hanna Ćwiek-Kupczyńska
- Institute of Plant Genetics, Polish Academy of Sciences, ul. Strzeszyńska 34, 60-479, Poznań, Poland
| | - Bert Droesbeke
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, 9052, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, Ghent, 9052, Belgium
| | - Richard Finkers
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ, the Netherlands
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, SI1000, Ljubljana, Slovenia
| | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Graham J King
- Southern Cross Plant Science, Southern Cross University, Lismore, NSW 2577, Australia
| | - Paweł Krajewski
- Institute of Plant Genetics, Polish Academy of Sciences, ul. Strzeszyńska 34, 60-479, Poznań, Poland
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Marie-Angélique Laporte
- Bioversity International, Parc Scientifique Agropolis II, Montpellier Cedex 5, 34397, France
| | - Célia Michotey
- Université Paris-Saclay, INRAE, URGI, Versailles, 78026, France
| | - Markus Oppermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Richard Ostler
- Computational and Analytical Sciences, Rothamsted Research, Harpenden, AL5 2JQ, UK
| | - Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany
- Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
| | | | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, SI1000, Ljubljana, Slovenia
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - François Tardieu
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, UMR759, Montpellier, 34060, France
| | - Cristobal Uauy
- Department of Crop Genetics, John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, UK
| | - Björn Usadel
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany
- Institute for Biology I, BioSC, RWTH Aachen University, Worringer Weg 3, 52074, Aachen, Germany
| | - Richard G F Visser
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ, the Netherlands
| | - Stephan Weise
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | | | - Célia M Miguel
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, 2780-157, Oeiras, Portugal
- BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal
| | | | - Cyril Pommier
- Université Paris-Saclay, INRAE, URGI, Versailles, 78026, France
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Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I, Coppens F, Cornut G, Costa BV, Ćwiek‐Kupczyńska H, Droesbeke B, Finkers R, Gruden K, Junker A, King GJ, Krajewski P, Lange M, Laporte M, Michotey C, Oppermann M, Ostler R, Poorter H, Ramírez‐Gonzalez R, Ramšak Ž, Reif JC, Rocca‐Serra P, Sansone S, Scholz U, Tardieu F, Uauy C, Usadel B, Visser RGF, Weise S, Kersey PJ, Miguel CM, Adam‐Blondon A, Pommier C. Enabling reusability of plant phenomic datasets with MIAPPE 1.1. THE NEW PHYTOLOGIST 2020; 227:260-273. [PMID: 32171029 PMCID: PMC7317793 DOI: 10.1111/nph.16544] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/24/2020] [Indexed: 05/21/2023]
Abstract
Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.
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Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I, Coppens F, Cornut G, Costa BV, Ćwiek-Kupczyńska H, Droesbeke B, Finkers R, Gruden K, Junker A, King GJ, Krajewski P, Lange M, Laporte MA, Michotey C, Oppermann M, Ostler R, Poorter H, Ramı Rez-Gonzalez R, Ramšak Ž, Reif JC, Rocca-Serra P, Sansone SA, Scholz U, Tardieu F, Uauy C, Usadel B, Visser RGF, Weise S, Kersey PJ, Miguel CM, Adam-Blondon AF, Pommier C. Enabling reusability of plant phenomic datasets with MIAPPE 1.1. THE NEW PHYTOLOGIST 2020. [PMID: 32171029 DOI: 10.15454/ah6u4a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.
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Affiliation(s)
- Evangelia A Papoutsoglou
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ, the Netherlands
| | - Daniel Faria
- BioData.pt, Instituto Gulbenkian de Ciência, 2780-156, Oeiras, Portugal
- INESC-ID, 1000-029, Lisboa, Portugal
| | - Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Elizabeth Arnaud
- Bioversity International, Parc Scientifique Agropolis II, Montpellier Cedex 5, 34397, France
| | - Ioannis N Athanasiadis
- Geo-Information Science and Remote Sensing Laboratory, Wageningen University, Droevendaalsesteeg 3, Wageningen, 6708PB, the Netherlands
| | - Inês Chaves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, 2780-157, Oeiras, Portugal
- Instituto de Biologia Experimental e Tecnológica (iBET), 2780-157, Oeiras, Portugal
| | - Frederik Coppens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, 9052, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, Ghent, 9052, Belgium
| | | | - Bruno V Costa
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, 2780-157, Oeiras, Portugal
- BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal
| | - Hanna Ćwiek-Kupczyńska
- Institute of Plant Genetics, Polish Academy of Sciences, ul. Strzeszyńska 34, 60-479, Poznań, Poland
| | - Bert Droesbeke
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, 9052, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, Ghent, 9052, Belgium
| | - Richard Finkers
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ, the Netherlands
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, SI1000, Ljubljana, Slovenia
| | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Graham J King
- Southern Cross Plant Science, Southern Cross University, Lismore, NSW 2577, Australia
| | - Paweł Krajewski
- Institute of Plant Genetics, Polish Academy of Sciences, ul. Strzeszyńska 34, 60-479, Poznań, Poland
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Marie-Angélique Laporte
- Bioversity International, Parc Scientifique Agropolis II, Montpellier Cedex 5, 34397, France
| | - Célia Michotey
- Université Paris-Saclay, INRAE, URGI, Versailles, 78026, France
| | - Markus Oppermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Richard Ostler
- Computational and Analytical Sciences, Rothamsted Research, Harpenden, AL5 2JQ, UK
| | - Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany
- Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
| | | | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, SI1000, Ljubljana, Slovenia
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | - François Tardieu
- INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, UMR759, Montpellier, 34060, France
| | - Cristobal Uauy
- Department of Crop Genetics, John Innes Centre, Norwich Research Park, Colney, Norwich, NR4 7UH, UK
| | - Björn Usadel
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, D-52425, Jülich, Germany
- Institute for Biology I, BioSC, RWTH Aachen University, Worringer Weg 3, 52074, Aachen, Germany
| | - Richard G F Visser
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ, the Netherlands
| | - Stephan Weise
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466, Seeland, Germany
| | | | - Célia M Miguel
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA) Avenida da República, 2780-157, Oeiras, Portugal
- BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal
| | | | - Cyril Pommier
- Université Paris-Saclay, INRAE, URGI, Versailles, 78026, France
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Ciasca B, Lanubile A, Marocco A, Pascale M, Logrieco AF, Lattanzio VMT. Application of an Integrated and Open Source Workflow for LC-HRMS Plant Metabolomics Studies. Case-Control Study: Metabolic Changes of Maize in Response to Fusarium verticillioides Infection. FRONTIERS IN PLANT SCIENCE 2020; 11:664. [PMID: 32582236 PMCID: PMC7290002 DOI: 10.3389/fpls.2020.00664] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 04/29/2020] [Indexed: 06/01/2023]
Abstract
Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) represents the most powerful metabolomics platform to investigate biological systems. Reproducible and standardized workflows allow obtaining a meaningful biological interpretation. The purpose of this study was to set up and apply an open-source workflow for LC-HRMS plant metabolomics studies. Key steps of the proposed workflow were as follows: (1) experimental design, (2) sample preparation, (3) LC-HRMS analysis, (4) data processing, (5) custom database search, (6) statistical analysis, (7) compound identification, and (8) biochemical interpretation. Its applicability was evaluated through the study of metabolomics changes of two maize recombinant inbred lines with contrasting phenotypes with respect to disease severity after Fusarium verticillioides infection of seedlings. Analysis of data from the case-control study revealed abundance change in metabolites belonging to different metabolic pathways, including two amino acids (L-tryptophan and tyrosine), five flavonoids, and three N-hydroxynnamic acid amides.
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Affiliation(s)
- Biancamaria Ciasca
- Institute of Sciences of Food Production, National Research Council, Bari, Italy
| | - Alessandra Lanubile
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Adriano Marocco
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Michelangelo Pascale
- Institute of Sciences of Food Production, National Research Council, Bari, Italy
| | - Antonio F. Logrieco
- Institute of Sciences of Food Production, National Research Council, Bari, Italy
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71
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Wang Q, Tang J, Han B, Huang X. Advances in genome-wide association studies of complex traits in rice. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:1415-1425. [PMID: 31720701 DOI: 10.1007/s00122-019-03473-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 11/05/2019] [Indexed: 05/27/2023]
Abstract
Genome-wide association studies (GWAS), genetic surveys of the whole genome to detect variants associated with a trait in natural populations, are a powerful approach for dissecting complex traits. This genetic mapping approach has been applied in rice over the last 10 years. During the last decade, GWAS was used to identify the loci underlying tens of rice traits, and several important genes were detected in GWAS and further confirmed in follow-up functional experiments. In this review, we present an overview of the whole process in a typical GWAS, including population design, genotyping, phenotyping and analysis methods. Recent advances in rice GWAS are also provided, including several examples of the functional characterization of candidate genes. The possible breakthroughs of rice GWAS in the next decade are discussed with regard to their application in breeding, the consideration of epistatic interactions and in-depth functional annotations of DNA elements and genetic variants throughout the rice genome.
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Affiliation(s)
- Qin Wang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Jiali Tang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence of Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200233, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, 200234, China.
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72
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Nvsvrot T, Xia W, Xiao Z, Zhan C, Liu M, Yang X, Zhang Y, Wang N. Combining QTL Mapping with Genome Resequencing Identifies an Indel in an R Gene that is Associated with Variation in Leaf Rust Disease Resistance in Poplar. PHYTOPATHOLOGY 2020; 110:900-906. [PMID: 31958037 DOI: 10.1094/phyto-10-19-0402-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Poplar trees (Populus spp.) are important and are widely grown worldwide. However, the extensive occurrence of leaf rust disease caused by Melampsora spp. seriously inhibits their growth and reduces their biomass. In our previous study, a high-quality genetic map was constructed for the poplar F1 population I-69 × XYY by using next-generation sequencing-based genotyping-by-sequencing. Here, we collected phenotypic data on leaf rust disease resistance on three different dates for all 300 progenies of the F1 population. Combining a high-quality genetic map and phenotypic data, we were able to detect 11 major quantitative trait loci (QTLs) for leaf rust disease resistance. Among these 11 QTLs, two pairs were detected on at least two dates. In the corresponding genomic sequence, we found that resistance (R) gene clusters were located in these two QTL regions. By using genome resequencing, PCR confirmation and statistical analysis, a 611-bp deletion within an R gene in one QTL region was found to be associated with variation in leaf rust disease resistance. A PCR-based examination of this 611-bp deletion was performed. This 611-bp deletion was also found to affect mRNA splicing and form a new protein with the loss of some key protein domains. Based on this study, we were able to determine the genetic architecture of variation in poplar leaf rust disease resistance, and the 611-bp deletion in the R gene could be used as a diagnostic marker for future poplar molecular breeding.
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Affiliation(s)
- Tashbek Nvsvrot
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Engineering Technology Research Center for Forestry Information, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenxiu Xia
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
- Logistics Service Group, Wuhan University, Wuhan, 430070, China
| | - Zheng'ang Xiao
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chang Zhan
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Meifeng Liu
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaoqing Yang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yan Zhang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Nian Wang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Engineering Technology Research Center for Forestry Information, Huazhong Agricultural University, Wuhan, 430070, China
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73
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Falque M, Jebreen K, Paux E, Knaak C, Mezmouk S, Martin OC. CNVmap: A Method and Software To Detect and Map Copy Number Variants from Segregation Data. Genetics 2020; 214:561-576. [PMID: 31882400 PMCID: PMC7054022 DOI: 10.1534/genetics.119.302881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/23/2019] [Indexed: 01/22/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) are used widely for detecting quantitative trait loci, or for searching for causal variants of diseases. Nevertheless, structural variations such as copy-number variants (CNVs) represent a large part of natural genetic diversity, and contribute significantly to trait variation. Numerous methods and softwares based on different technologies (amplicons, CGH, tiling, or SNP arrays, or sequencing) have already been developed to detect CNVs, but they bypass a wealth of information such as genotyping data from segregating populations, produced, e.g., for QTL mapping. Here, we propose an original method to both detect and genetically map CNVs using mapping panels. Specifically, we exploit the apparent heterozygous state of duplicated loci: peaks in appropriately defined genome-wide allelic profiles provide highly specific signatures that identify the nature and position of the CNVs. Our original method and software can detect and map automatically up to 33 different predefined types of CNVs based on segregation data only. We validate this approach on simulated and experimental biparental mapping panels in two maize populations and one wheat population. Most of the events found correspond to having just one extra copy in one of the parental lines, but the corresponding allelic value can be that of either parent. We also find cases with two or more additional copies, especially in wheat, where these copies locate to homeologues. More generally, our computational tool can be used to give additional value, at no cost, to many datasets produced over the past decade from genetic mapping panels.
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Affiliation(s)
- Matthieu Falque
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
| | - Kamel Jebreen
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
- Department of Mathematics, An-Najah National University, Nablus, Palestine
| | - Etienne Paux
- Université Clermont Auvergne, INRAE, GDEC, 63000 Clermont-Ferrand, France
| | | | | | - Olivier C Martin
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190 Gif-sur-Yvette, France
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Qu P, Shi J, Chen T, Chen K, Shen C, Wang J, Zhao X, Ye G, Xu J, Zhang L. Construction and integration of genetic linkage maps from three multi-parent advanced generation inter-cross populations in rice. RICE (NEW YORK, N.Y.) 2020; 13:13. [PMID: 32060661 PMCID: PMC7021868 DOI: 10.1186/s12284-020-0373-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 02/04/2020] [Indexed: 05/08/2023]
Abstract
BACKGROUND The construction of genetic maps based on molecular markers is a crucial step in rice genetic and genomic studies. Pure lines derived from multiple parents provide more abundant genetic variation than those from bi-parent populations. Two four-parent pure-line populations (4PL1 and 4PL2) and one eight-parent pure-line population (8PL) were developed from eight homozygous indica varieties of rice by the International Rice Research Institute (IRRI). To the best of our knowledge, there have been no reports on linkage map construction and their integration in multi-parent populations of rice. RESULTS We constructed linkage maps for the three multi-parent populations and conducted quantitative trait locus (QTL) mapping for heading date (HD) and plant height (PH) based on the three maps by inclusive composite interval mapping (ICIM). An integrated map was built from the three individual maps and used for QTL projection and meta-analysis. QTL mapping of the three populations was also conducted based on the integrated map, and the mapping results were compared with those from meta-analysis. The three linkage maps developed for 8PL, 4PL1 and 4PL2 had 5905, 4354 and 5464 bins and were 1290.16, 1720.01 and 1560.30 cM in length, respectively. The integrated map was 3022.08 cM in length and contained 10,033 bins. Based on the three linkage maps, 3, 7 and 9 QTLs were detected for HD while 6, 9 and 10 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. In contrast, 19 and 25 QTLs were identified for HD and PH by meta-analysis using the integrated map, respectively. Based on the integrated map, 5, 9, and 10 QTLs were detected for HD while 3, 10, and 12 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. Eleven of these 49 QTLs coincided with those from the meta-analysis. CONCLUSIONS In this study, we reported the first rice linkage map constructed from one eight-parent recombinant inbred line (RIL) population and the first integrated map from three multi-parent populations, which provide essential information for QTL linkage mapping, meta-analysis, and map-based cloning in rice genetics and breeding.
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Affiliation(s)
| | | | - Tianxiao Chen
- Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, 518210, China
| | - Kai Chen
- Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, 518210, China
| | - Congcong Shen
- Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, 518210, China
| | - Jiankang Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiangqian Zhao
- Institute of Crop Science and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Science, Hangzhou, 310021, China
| | - Guoyou Ye
- Genetics and Biotechnology Division, International Rice Research Institute, Baños, Laguna, Philippines
| | - Jianlong Xu
- The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Luyan Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Yi Q, Malvar RA, Álvarez-Iglesias L, Ordás B, Revilla P. Dissecting the genetics of cold tolerance in a multiparental maize population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:503-516. [PMID: 31740990 DOI: 10.1007/s00122-019-03482-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 11/11/2019] [Indexed: 05/21/2023]
Abstract
We identify the largest amount of QTLs for cold tolerance in maize; mainly associated with photosynthetic efficiency, which opens new possibilities for genomic selection for cold tolerance in maize. Breeding for cold tolerance in maize is an important objective in temperate areas. The objective was to carry out a highly efficient study of quantitative trait loci (QTLs) for cold tolerance in maize. We evaluated 406 recombinant inbred lines from a multi-parent advanced generation intercross (MAGIC) population in a growth chamber under cold and control conditions, and in the field at early and normal sowing. We recorded cold tolerance-related traits, including the number of days from sowing to emergence, chlorophyll content and maximum quantum efficiency of photosystem II (Fv/Fm). Association mapping was based on genotyping with near one million single nucleotide polymorphism (SNP) markers. We found 858 SNPs significantly associated with all traits, most of them under cold conditions and early sowing. Most QTLs were associated with chlorophyll and Fv/Fm. Many candidate genes coincided between the current research and previous reports. These results suggest that (1) the MAGIC population is an efficient tool for identifying QTLs for cold tolerance; (2) most QTLs for cold tolerance were associated with Fv/Fm; (3) most of these QTLs were located in specific genomic regions, particularly bin 10.04; (4) the current study allows genetically improving cold tolerance with genome-wide selection.
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Affiliation(s)
- Q Yi
- Misión Biológica de Galicia (CSIC), Apartado 28, 36080, Pontevedra, Spain
- College of Agronomy, Sichuan Agricultural University, Chengdu, 611130, China
| | - R A Malvar
- Misión Biológica de Galicia (CSIC), Apartado 28, 36080, Pontevedra, Spain
| | - L Álvarez-Iglesias
- Misión Biológica de Galicia (CSIC), Apartado 28, 36080, Pontevedra, Spain
| | - B Ordás
- Misión Biológica de Galicia (CSIC), Apartado 28, 36080, Pontevedra, Spain
| | - Pedro Revilla
- Misión Biológica de Galicia (CSIC), Apartado 28, 36080, Pontevedra, Spain.
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76
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Coffman SM, Hufford MB, Andorf CM, Lübberstedt T. Haplotype structure in commercial maize breeding programs in relation to key founder lines. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:547-561. [PMID: 31749017 DOI: 10.1007/s00122-019-03486-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 11/13/2019] [Indexed: 05/05/2023]
Abstract
High-density haplotype analysis revealed significant haplotype sharing between ex-PVPs registered from 1976 to 1992 and key maize founders, and uncovered similarities and differences in haplotype sharing patterns by company and heterotic group. Proprietary inbreds developed by the private seed industry have been the major source for driving genetic gain in successful North American maize hybrids for decades. Much of the history of industry germplasm can be traced back to key founder lines, some of which were pivotal in the development of prominent heterotic groups. Previous studies have summarized pedigree-based relationships, genetic diversity and population structure among commercial inbreds with expired Plant Variety Protection (ex-PVP). However, less is known about the extent of haplotype sharing between historical founders and ex-PVPs. A better understanding of the relationships between founders and ex-PVPs provides insight into the haplotype and heterotic group structure among industry germplasm. We performed high-density haplotype analysis with 11.3 million SNPs on 212 maize inbreds, which included 157 ex-PVPs registered 1976-1992 and 55 public lines relevant to PVPs. Among these lines were 12 key founders identified in literature review: 207, A632, B14, B37, B73, LH123HT, LH82, Mo17, Oh43, OH7, PHG39 and Wf9. Our results revealed that, on average, 81.6% of an ex-PVP's genome is shared with at least 1 of these 12 founder lines and more than half when limited to B73, Mo17 and 207. Quantifiable similarities and contrasts among heterotic groups and major US seed industry companies were also observed. The results from this study provide high-resolution haplotype data on ex-PVP germplasm, confirm founder relationship trends observed in previous studies, uncover region-specific haplotype structure differences and demonstrate how haplotype sharing analysis can be used as a tool to explore germplasm diversity.
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Affiliation(s)
- Stephanie M Coffman
- Systems and Innovation for Breeding and Seed Products, Corteva Agriscience™, Agriculture Division of DowDuPont™, 8305 NW 62nd Ave., P.O. Box 7060, Johnston, IA, 50131, USA.
- Department of Agronomy, Iowa State University, Ames, IA, 50011, USA.
| | - Matthew B Hufford
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Carson M Andorf
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, 50011, USA
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Liu HJ, Wang X, Xiao Y, Luo J, Qiao F, Yang W, Zhang R, Meng Y, Sun J, Yan S, Peng Y, Niu L, Jian L, Song W, Yan J, Li C, Zhao Y, Liu Y, Warburton ML, Zhao J, Yan J. CUBIC: an atlas of genetic architecture promises directed maize improvement. Genome Biol 2020; 21:20. [PMID: 31980033 PMCID: PMC6979394 DOI: 10.1186/s13059-020-1930-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/08/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. RESULTS Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. CONCLUSIONS Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.
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Affiliation(s)
- Hai-Jun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xiaqing Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Feng Qiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Sanming Academy of Agricultural Sciences, Sanming, 365509, Fujian, China
| | - Wenyu Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ruyang Zhang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Yijiang Meng
- College of Life Science, Hebei Agricultural University, Baoding, 071001, China
| | - Jiamin Sun
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shijuan Yan
- Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Tianhe District, Guangzhou, 510640, China
| | - Yong Peng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Luyao Niu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Liumei Jian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wei Song
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Jiali Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chunhui Li
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Ya Liu
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Marilyn L Warburton
- Corn Host Plant Resistance Research Unit, United States Department of Agriculture-Agricultural Research Service, Box 9555, Mississippi State, MS, 39762, USA
| | - Jiuran Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China.
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
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78
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Guo D, Jiang H, Yan W, Yang L, Ye J, Wang Y, Yan Q, Chen J, Gao Y, Duan L, Liu H, Xie L. Resequencing 200 Flax Cultivated Accessions Identifies Candidate Genes Related to Seed Size and Weight and Reveals Signatures of Artificial Selection. FRONTIERS IN PLANT SCIENCE 2020; 10:1682. [PMID: 32010166 PMCID: PMC6976528 DOI: 10.3389/fpls.2019.01682] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 11/29/2019] [Indexed: 05/13/2023]
Abstract
Seed size and weight are key traits determining crop yield, which often undergo strongly artificial selection during crop domestication. Although seed sizes differ significantly between oil flax and fiber flax, the genetic basis of morphological differences and artificial selection characteristics in seed size remains largely unclear. Here we re-sequenced 200 flax cultivated accessions to generate a genome variation map based on chromosome assembly reference genomes. We provide evidence that oil flax group is the ancestor of cultivated flax, and the oil-fiber dual purpose group (OF) is the evolutionary intermediate transition state between oil and fiber flax. Genome-wide association studies (GWAS) were combined with LD Heatmap to identify candidate regions related to seed size and weight, then candidate genes were screened based on detailed functional annotations and estimation of nucleotide polymorphism effects. Using this strategy, we obtained 13 candidate genes related to seed size and weight. Selective sweeps analysis indicates human-involved selection of small seeds during the oil to fiber flax transition. Our study shows the existence of elite alleles for seed size and weight in flax germplasm and provides molecular insights into approaches for further improvement.
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Affiliation(s)
- Dongliang Guo
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Haixia Jiang
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Wenliang Yan
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetics and Breeding for Soybean, Ministry of Agriculture, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Liangjie Yang
- Herbal Medicine Innovation Research Center, Agricultural Bureau of Zhaosu County, Yili, China
| | - Jiali Ye
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Yue Wang
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Qingcheng Yan
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Jiaxun Chen
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Yanfang Gao
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Lepeng Duan
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Huiqing Liu
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
| | - Liqiong Xie
- National Center of Melon Engineering and Technology, Molecular Breeding Laboratory, College of Life Science and Technology, Xinjiang University, Urumqi, China
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79
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Han Z, Hu G, Liu H, Liang F, Yang L, Zhao H, Zhang Q, Li Z, Zhang Q, Xing Y. Bin-based genome-wide association analyses improve power and resolution in QTL mapping and identify favorable alleles from multiple parents in a four-way MAGIC rice population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:59-71. [PMID: 31549182 DOI: 10.1007/s00122-019-03440-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 09/17/2019] [Indexed: 05/25/2023]
Abstract
A whole genome bin map was developed for a MAGIC population. Association studies for heading date at bin level exhibited powerful QTL mapping and identified favorable alleles. The presumed advantages of multiparent advanced generation intercross (MAGIC) population in quantitative trait locus (QTL) mapping were not fully utilized in the previous studies in which genome-wide association studies (GWAS) were conducted at only single nucleotide polymorphism level. In this study, we genotyped a rice four-way MAGIC population of 247 F7 lines and their parents by sequencing. A total of 5934 bins with an average length of 65 kb were constructed and covered 97% of the genome. The MAGIC population showed low population structure and balanced parental contributions. A bin-based GWAS for heading date identified 4 QTLs in three environments. Three major QTLs were mapped exactly to the bins where the major heading date genes DTH3, Ghd7.1 and Ghd8 were located. Multiple comparisons showed that different parental alleles had varied genetic effects. Like DTH3, the alleles of the Guichao 2/YJSM, IR34 and Cypress had larger, intermediate and no effects, respectively. Based on comparative sequencing of 8 known heading date genes undetected in this MAGIC population, only Ghd7 exhibited diverse function among parents. The failure in Ghd7 mapping was well explained by its interaction with Hd1 because Ghd7 had no effects on heading date when combined with the nonfunctional hd1 carried by all four parents. Overall, bin-based GWAS have more mapping power and higher resolution with a MAGIC population and provide favorable alleles to breeders. The use of more diversified parents is encouraged to develop a MAGIC population for detecting more QTLs for important agronomical traits.
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Affiliation(s)
- Zhongmin Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China
| | - Gang Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China
| | - Hua Liu
- College of Agriculture, Yangtze University, Hubei Collaborative Innovation Center for Grain Industry, Jingzhou, 434000, China
| | - Famao Liang
- College of Agriculture, Yangtze University, Hubei Collaborative Innovation Center for Grain Industry, Jingzhou, 434000, China
| | - Lin Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China
| | - Zhixin Li
- College of Agriculture, Yangtze University, Hubei Collaborative Innovation Center for Grain Industry, Jingzhou, 434000, China
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070, China.
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80
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Luo J, Wei C, Liu H, Cheng S, Xiao Y, Wang X, Yan J, Liu J. MaizeCUBIC: a comprehensive variation database for a maize synthetic population. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5857845. [PMID: 32548639 PMCID: PMC7297647 DOI: 10.1093/database/baaa044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/03/2020] [Accepted: 05/18/2020] [Indexed: 11/13/2022]
Abstract
MaizeCUBIC is a free database that describes genomic variations, gene expression, phenotypes and quantitative trait locus (QTLs) for a maize CUBIC population (24 founders and 1404 inbred offspring). The database not only includes information for over 14M single nucleotide polymorphism (SNPs) and 43K indels previously identified but also contains 660K structure variations (SVs) and 600M novel sequences newly identified in the present study, which represents a comprehensive high-density variant map for a diverse population. Based on these genomic variations, the database would demonstrate the mosaic structure for each progeny, reflecting a high-resolution reshuffle across parental genomes. A total of 23 agronomic traits measured on parents and progeny in five locations, where are representative of the maize main growing regions in China, were also included in the database. To further explore the genotype–phenotype relationships, two different methods of genome-wide association studies (GWAS) were employed for dissecting the genetic architecture of 23 agronomic traits. Additionally, the Basic Local Alignment Search Tool and primer design tools are developed to promote follow-up analysis and experimental verification. All the original data and corresponding analytical results can be accessed through user-friendly online queries and web interface dynamic visualization, as well as downloadable files. These data and tools provide valuable resources on genetic and genomic studies of maize and other crops.
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Affiliation(s)
- Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Chengcheng Wei
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Haijun Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.,Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter, Vienna 1030, Austria
| | - Shikun Cheng
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaqing Wang
- 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
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.,College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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81
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Chen Q, Yang CJ, York AM, Xue W, Daskalska LL, DeValk CA, Krueger KW, Lawton SB, Spiegelberg BG, Schnell JM, Neumeyer MA, Perry JS, Peterson AC, Kim B, Bergstrom L, Yang L, Barber IC, Tian F, Doebley JF. TeoNAM: A Nested Association Mapping Population for Domestication and Agronomic Trait Analysis in Maize. Genetics 2019; 213:1065-1078. [PMID: 31481533 PMCID: PMC6827374 DOI: 10.1534/genetics.119.302594] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 08/30/2019] [Indexed: 12/21/2022] Open
Abstract
Recombinant inbred lines (RILs) are an important resource for mapping genes controlling complex traits in many species. While RIL populations have been developed for maize, a maize RIL population with multiple teosinte inbred lines as parents has been lacking. Here, we report a teosinte nested association mapping (TeoNAM) population, derived from crossing five teosinte inbreds to the maize inbred line W22. The resulting 1257 BC1S4 RILs were genotyped with 51,544 SNPs, providing a high-density genetic map with a length of 1540 cM. On average, each RIL is 15% homozygous teosinte and 8% heterozygous. We performed joint linkage mapping (JLM) and a genome-wide association study (GWAS) for 22 domestication and agronomic traits. A total of 255 QTL from JLM were identified, with many of these mapping near known genes or novel candidate genes. TeoNAM is a useful resource for QTL mapping for the discovery of novel allelic variation from teosinte. TeoNAM provides the first report that PROSTRATE GROWTH1, a rice domestication gene, is also a QTL associated with tillering in teosinte and maize. We detected multiple QTL for flowering time and other traits for which the teosinte allele contributes to a more maize-like phenotype. Such QTL could be valuable in maize improvement.
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Affiliation(s)
- Qiuyue Chen
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
- National Maize Improvement Center, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, Joint International Research Laboratory of Crop Molecular Breeding, China Agricultural University, Beijing 100193, China
| | - Chin Jian Yang
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Alessandra M York
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Wei Xue
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Lora L Daskalska
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Craig A DeValk
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Kyle W Krueger
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Samuel B Lawton
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | | | - Jack M Schnell
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Michael A Neumeyer
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Joseph S Perry
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Aria C Peterson
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Brandon Kim
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Laura Bergstrom
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Liyan Yang
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
- School of Life Science, Shanxi Normal University, Linfen, Shanxi 041004, China
| | - Isaac C Barber
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
| | - Feng Tian
- National Maize Improvement Center, Key Laboratory of Biology and Genetic Improvement of Maize (MOA), Beijing Key Laboratory of Crop Genetic Improvement, Joint International Research Laboratory of Crop Molecular Breeding, China Agricultural University, Beijing 100193, China
| | - John F Doebley
- Laboratory of Genetics, University of Wisconsin-Madison, Wisconsin 53706
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82
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Scaglione D, Pinosio S, Marroni F, Di Centa E, Fornasiero A, Magris G, Scalabrin S, Cattonaro F, Taylor G, Morgante M. Single primer enrichment technology as a tool for massive genotyping: a benchmark on black poplar and maize. ANNALS OF BOTANY 2019; 124:543-552. [PMID: 30932149 DOI: 10.1093/aob/mcz054/5424191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 03/25/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND AND AIMS The advent of molecular breeding is advocated to improve the productivity and sustainability of second-generation bioenergy crops. Advanced molecular breeding in bioenergy crops relies on the ability to massively sample the genetic diversity. Genotyping-by-sequencing has become a widely adopted method for cost-effective genotyping. It basically requires no initial investment for design as compared with array-based platforms which have been shown to offer very robust assays. The latter, however, has the drawback of being limited to analyse only the genetic diversity accounted during selection of a set of polymorphisms and design of the assay. In contrast, genotyping-by-sequencing with random sampling of genomic loci via restriction enzymes or random priming has been shown to be fast and convenient but lacks the ability to target specific regions of the genome and to maintain high reproducibility across laboratories. METHODS Here we present a first adoption of single-primer enrichment technology (SPET) which provides a highly efficient and scalable system to obtain targeted sequence-based large genotyping data sets, bridging the gaps between array-based systems and traditional sequencing-based protocols. To fully explore SPET performance, we conducted a benchmark study in ten Zea mays lines and a large-scale study of a natural black poplar population of 540 individuals with the aim of discovering polymorphisms associated with biomass-related traits. KEY RESULTS Our results showed the ability of this technology to provide dense genotype information on a customized panel of selected polymorphisms, while yielding hundreds of thousands of untargeted variable sites. This provided an ideal resource for association analysis of natural populations harbouring unexplored allelic diversities and structure such as in black poplar. CONCLUSION The improvement of sequencing throughput and the development of efficient library preparation methods has made it feasible to carry out targeted genotyping-by-sequencing experiments cost-competitively with either random complexity reduction systems or traditional array-based platforms, while maintaining the key advantages of both technologies.
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Affiliation(s)
- Davide Scaglione
- IGA Technology Services s.r.l., via Jacopo Linussio, Udine, Italy
| | - Sara Pinosio
- IGA - Istituto di Genomica Applicata, via Jacopo Linussio, Udine, Italy
- Institute of Biosciences and Bioresources, National Research Council, Via Madonna del Piano, Sesto Fiorentino, Firenze, Italy
| | - Fabio Marroni
- IGA Technology Services s.r.l., via Jacopo Linussio, Udine, Italy
| | | | - Alice Fornasiero
- IGA - Istituto di Genomica Applicata, via Jacopo Linussio, Udine, Italy
| | - Gabriele Magris
- Dipartimento di Scienze Agro-alimentari, Università di Udine, Ambientali e Animali (DI4A), Udine, Italy
| | - Simone Scalabrin
- IGA Technology Services s.r.l., via Jacopo Linussio, Udine, Italy
| | | | - Gail Taylor
- Centre for Biological Sciences, Life Sciences Building, University of Southampton, Southampton, UK
| | - Michele Morgante
- IGA - Istituto di Genomica Applicata, via Jacopo Linussio, Udine, Italy
- Dipartimento di Scienze Agro-alimentari, Università di Udine, Ambientali e Animali (DI4A), Udine, Italy
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83
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Scaglione D, Pinosio S, Marroni F, Di Centa E, Fornasiero A, Magris G, Scalabrin S, Cattonaro F, Taylor G, Morgante M. Single primer enrichment technology as a tool for massive genotyping: a benchmark on black poplar and maize. ANNALS OF BOTANY 2019; 124:543-552. [PMID: 30932149 PMCID: PMC6821380 DOI: 10.1093/aob/mcz054] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 03/25/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND AND AIMS The advent of molecular breeding is advocated to improve the productivity and sustainability of second-generation bioenergy crops. Advanced molecular breeding in bioenergy crops relies on the ability to massively sample the genetic diversity. Genotyping-by-sequencing has become a widely adopted method for cost-effective genotyping. It basically requires no initial investment for design as compared with array-based platforms which have been shown to offer very robust assays. The latter, however, has the drawback of being limited to analyse only the genetic diversity accounted during selection of a set of polymorphisms and design of the assay. In contrast, genotyping-by-sequencing with random sampling of genomic loci via restriction enzymes or random priming has been shown to be fast and convenient but lacks the ability to target specific regions of the genome and to maintain high reproducibility across laboratories. METHODS Here we present a first adoption of single-primer enrichment technology (SPET) which provides a highly efficient and scalable system to obtain targeted sequence-based large genotyping data sets, bridging the gaps between array-based systems and traditional sequencing-based protocols. To fully explore SPET performance, we conducted a benchmark study in ten Zea mays lines and a large-scale study of a natural black poplar population of 540 individuals with the aim of discovering polymorphisms associated with biomass-related traits. KEY RESULTS Our results showed the ability of this technology to provide dense genotype information on a customized panel of selected polymorphisms, while yielding hundreds of thousands of untargeted variable sites. This provided an ideal resource for association analysis of natural populations harbouring unexplored allelic diversities and structure such as in black poplar. CONCLUSION The improvement of sequencing throughput and the development of efficient library preparation methods has made it feasible to carry out targeted genotyping-by-sequencing experiments cost-competitively with either random complexity reduction systems or traditional array-based platforms, while maintaining the key advantages of both technologies.
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Affiliation(s)
- Davide Scaglione
- IGA Technology Services s.r.l., via Jacopo Linussio, Udine, Italy
- correspondence. E-mail
| | - Sara Pinosio
- IGA – Istituto di Genomica Applicata, via Jacopo Linussio, Udine, Italy
- Institute of Biosciences and Bioresources, National Research Council, Via Madonna del Piano, Sesto Fiorentino, Firenze, Italy
| | - Fabio Marroni
- IGA Technology Services s.r.l., via Jacopo Linussio, Udine, Italy
| | | | - Alice Fornasiero
- IGA – Istituto di Genomica Applicata, via Jacopo Linussio, Udine, Italy
| | - Gabriele Magris
- Dipartimento di Scienze Agro-alimentari, Università di Udine, Ambientali e Animali (DI4A), Udine, Italy
| | - Simone Scalabrin
- IGA Technology Services s.r.l., via Jacopo Linussio, Udine, Italy
| | | | - Gail Taylor
- Centre for Biological Sciences, Life Sciences Building, University of Southampton, Southampton, UK
| | - Michele Morgante
- IGA – Istituto di Genomica Applicata, via Jacopo Linussio, Udine, Italy
- Dipartimento di Scienze Agro-alimentari, Università di Udine, Ambientali e Animali (DI4A), Udine, Italy
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84
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Jiménez-Galindo JC, Malvar RA, Butrón A, Santiago R, Samayoa LF, Caicedo M, Ordás B. Mapping of resistance to corn borers in a MAGIC population of maize. BMC PLANT BIOLOGY 2019; 19:431. [PMID: 31623579 PMCID: PMC6796440 DOI: 10.1186/s12870-019-2052-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 09/24/2019] [Indexed: 05/05/2023]
Abstract
BACKGROUND Corn borers constitute an important pest of maize around the world; in particular Sesamia nonagrioides Lefèbvre, named Mediterranean corn borer (MCB), causes important losses in Southern Europe. Methods of selection can be combined with transgenic approaches to increase the efficiency and durability of the resistance to corn borers. Previous studies of the genetic factors involved in resistance to MCB have been carried out using bi-parental populations that have low resolution or using association inbred panels that have a low power to detect rare alleles. We developed a Multi-parent Advanced Generation InterCrosses (MAGIC) population to map with high resolution the genetic determinants of resistance to MCB. RESULTS We detected multiple single nucleotide polymorphisms (SNPs) of low effect associated with resistance to stalk tunneling by MCB. We dissected a wide region related to stalk tunneling in multiple studies into three smaller regions (at ~ 150, ~ 155, and ~ 165 Mb in chromosome 6) that closely overlap with regions associated with cell wall composition. We also detected regions associated with kernel resistance and agronomic traits, although the co-localization of significant regions between traits was very low. This indicates that it is possible the concurrent improvement of resistance and agronomic traits. CONCLUSIONS We developed a mapping population which allowed a finer dissection of the genetics of maize resistance to corn borers and a solid nomination of candidate genes based on functional information. The population, given its large variability, was also adequate to map multiple traits and study the relationship between them.
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Affiliation(s)
- José Cruz Jiménez-Galindo
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
- National Institute of Forestry, Agriculture and Livestock Research (INIFAP), Ave. Hidalgo 1213, Cd. Cuauhtémoc, 31500 Chihuahua, Mexico
| | - Rosa Ana Malvar
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
| | - Ana Butrón
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
| | - Rogelio Santiago
- Departamento Biología Vegetal y Ciencias del Suelo, Unidad Asociada BVE1-UVIGO y MBG (CSIC), Facultad de Biología, Universidad de Vigo, Campus As Lagoas Marcosende, 36310 Vigo, Spain
| | - Luis Fernando Samayoa
- North Carolina State University, 4210 Williams Hall 101, Derieux Place, Raleigh, NC 27695 USA
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695-7620 USA
| | - Marlon Caicedo
- Instituto Nacional de Investigaciones Agropecuarias (INIAP), 170315 Quito, Ecuador
| | - Bernardo Ordás
- Misión Biológica de Galicia, Spanish National Research Council (CSIC), Apartado 28, 36080 Pontevedra, Spain
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85
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Gyawali A, Shrestha V, Guill KE, Flint-Garcia S, Beissinger TM. Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs. BMC PLANT BIOLOGY 2019; 19:412. [PMID: 31590656 PMCID: PMC6781408 DOI: 10.1186/s12870-019-2000-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 08/30/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs. RESULTS In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960's, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA. CONCLUSION Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.
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Affiliation(s)
- Abiskar Gyawali
- Division of Biological Sciences, University of Missouri, Columbia, USA
| | - Vivek Shrestha
- Division of Biological Sciences, University of Missouri, Columbia, USA
| | | | - Sherry Flint-Garcia
- USDA-ARS, Columbia, MO USA
- Division of Plant Sciences, University of Missouri, Columbia, USA
| | - Timothy M. Beissinger
- Department of Crop Sciences, Georg-August Universität Göttingen, Göttingen, Germany
- Center for Integrated Breeding Research, Georg August Universität Göttingen, Göttingen, Germany
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86
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Tomkowiak A, Bocianowski J, Wolko Ł, Adamczyk J, Mikołajczyk S, Kowalczewski PŁ. Identification of Markers Associated with Yield Traits and Morphological Features in Maize ( Zea mays L.). PLANTS 2019; 8:plants8090330. [PMID: 31491958 PMCID: PMC6783969 DOI: 10.3390/plants8090330] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/30/2019] [Accepted: 09/03/2019] [Indexed: 12/05/2022]
Abstract
Association mapping is a powerful approach to detect associations between traits of interest and genetic markers based on linkage disequilibrium in molecular plant breeding. The aim of this study was the identification of single nucleotide polymorphisms (SNPs) and SilicoDArT markers associated with yield traits and morphological features in maize. Plant material constituted inbred lines. The field experiment with inbred lines was established on 10 m2 plots in a set of complete random blocks in three replicates. We observed 22 quantitative traits. Association mapping was performed in this study using a method based on the mixed linear model with the population structure estimated by eigenanalysis (principal component analysis applied to all markers) and modeled by random effects. As a result of mapping, 969 markers (346 SNPs and 623 SilocoDArT) were selected from 49,911 identified polymorphic molecular markers, which were significantly associated with the analyzed morphological features and yield structure traits. Markers associated with five or six traits were selected during further analyses, including SilicoDArT 4591115 (anthocyanin coloration of anthers, length of main axis above the highest lateral branch, cob length, number of grains per cob, weight of fresh grains per cob and weight of fresh grains per cob at 15% moisture), SilicoDArT 7059939 (anthocyanin coloration of glumes of cob, time of anthesis—50% of flowering plants, time of silk emergence—50% of flowering plants, anthocyanin coloration of anthers and cob diameter), SilicoDArT 5587991 (anthocyanin coloration of glumes of cob, time of anthesis—50% of flowering plants, anthocyanin coloration of anthers, curvature of lateral branches and number of rows of grain). The two genetic similarity dendrograms between the inbred lines were constructed based on all significant SNPs and SilicoDArT markers. On both dendrograms lines clustered according to the kernel structure (flint, dent) and origin. The selected markers may be useful in predicting hybrid formulas in a heterosis culture. The present study demonstrated that molecular SNP and Silico DArT markers could be used in this species to group lines in terms of origin and lines with incomplete origin data. They can also be useful in maize in predicting the hybrid formula and can find applications in the selection of parental components for heterosis crossings.
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Affiliation(s)
- Agnieszka Tomkowiak
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, 11 Dojazd St., 60-632 Poznań, Poland.
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, 28 Wojska Polskiego St., 60-637 Poznań, Poland.
| | - Łukasz Wolko
- Department of Biochemistry and Biotechnology, Poznań University of Life Sciences, 11 Dojazd St., 60-632 Poznań, Poland.
| | - Józef Adamczyk
- Plant Breeding Smolice Ltd., Co., Smolice 146, 63-740 Kobylin, Poland.
| | - Sylwia Mikołajczyk
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, 11 Dojazd St., 60-632 Poznań, Poland.
| | - Przemysław Łukasz Kowalczewski
- Institute of Food Technology of Plant Origin, Poznań University of Life Sciences, 31 Wojska Polskiego St., 60-624 Poznań, Poland.
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87
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Williams-Simon PA, Posey C, Mitchell S, Ng'oma E, Mrkvicka JA, Zars T, King EG. Multiple genetic loci affect place learning and memory performance in Drosophila melanogaster. GENES, BRAIN, AND BEHAVIOR 2019; 18:e12581. [PMID: 31095869 PMCID: PMC6718298 DOI: 10.1111/gbb.12581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/11/2019] [Accepted: 05/13/2019] [Indexed: 12/25/2022]
Abstract
Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used the Drosophila synthetic population resource (DSPR), a multiparent mapping resource in the model system Drosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a "heat box" to learn to remain on one side of a chamber (place learning) and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for ~49 000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA-Seq on pooled samples of seven high performing and seven low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.
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Affiliation(s)
| | - Christopher Posey
- Division of Biological Sciences, University of Missouri, Columbia, Missouri
| | - Samuel Mitchell
- Division of Biological Sciences, University of Missouri, Columbia, Missouri
| | - Enoch Ng'oma
- Division of Biological Sciences, University of Missouri, Columbia, Missouri
| | - James A Mrkvicka
- Division of Biological Sciences, University of Missouri, Columbia, Missouri
| | - Troy Zars
- Division of Biological Sciences, University of Missouri, Columbia, Missouri
| | - Elizabeth G King
- Division of Biological Sciences, University of Missouri, Columbia, Missouri
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88
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Zheng C, Boer MP, van Eeuwijk FA. Construction of Genetic Linkage Maps in Multiparental Populations. Genetics 2019; 212:1031-1044. [PMID: 31182487 PMCID: PMC6707453 DOI: 10.1534/genetics.119.302229] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 06/04/2019] [Indexed: 11/18/2022] Open
Abstract
Construction of genetic linkage maps has become a routine step for mapping quantitative trait loci (QTL), particularly in animal and plant breeding populations. Many multiparental populations have recently been produced to increase genetic diversity and QTL mapping resolution. However, few software packages are available for map construction in these populations. In this paper, we build a general framework for the construction of genetic linkage maps from genotypic data in diploid populations, including bi- and multiparental populations, cross-pollinated (CP) populations, and breeding pedigrees. The framework is implemented as an automatic pipeline called magicMap, where the maximum multilocus likelihood approach utilizes genotypic information efficiently. We evaluate magicMap by extensive simulations and eight real datasets: one biparental, one CP, four multiparent advanced generation intercross (MAGIC), and two nested association mapping (NAM) populations, the number of markers ranging from a few hundred to tens of thousands. Not only is magicMap the only software capable of accommodating all of these designs, it is more accurate and robust to missing genotypes and genotyping errors than commonly used packages.
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Affiliation(s)
- Chaozhi Zheng
- Biometris, Wageningen University and Research, 6700 AA, The Netherlands
| | - Martin P Boer
- Biometris, Wageningen University and Research, 6700 AA, The Netherlands
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89
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Kidane YG, Gesesse CA, Hailemariam BN, Desta EA, Mengistu DK, Fadda C, Pè ME, Dell'Acqua M. A large nested association mapping population for breeding and quantitative trait locus mapping in Ethiopian durum wheat. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:1380-1393. [PMID: 30575264 PMCID: PMC6576139 DOI: 10.1111/pbi.13062] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 12/11/2018] [Accepted: 12/15/2018] [Indexed: 05/11/2023]
Abstract
The Ethiopian plateau hosts thousands of durum wheat (Triticum turgidum subsp. durum) farmer varieties (FV) with high adaptability and breeding potential. To harness their unique allelic diversity, we produced a large nested association mapping (NAM) population intercrossing fifty Ethiopian FVs with an international elite durum wheat variety (Asassa). The Ethiopian NAM population (EtNAM) is composed of fifty interconnected bi-parental families, totalling 6280 recombinant inbred lines (RILs) that represent both a powerful quantitative trait loci (QTL) mapping tool, and a large pre-breeding panel. Here, we discuss the molecular and phenotypic diversity of the EtNAM founder lines, then we use an array featuring 13 000 single nucleotide polymorphisms (SNPs) to characterize a subset of 1200 EtNAM RILs from 12 families. Finally, we test the usefulness of the population by mapping phenology traits and plant height using a genome wide association (GWA) approach. EtNAM RILs showed high allelic variation and a genetic makeup combining genetic diversity from Ethiopian FVs with the international durum wheat allele pool. EtNAM SNP data were projected on the fully sequenced AB genome of wild emmer wheat, and were used to estimate pairwise linkage disequilibrium (LD) measures that reported an LD decay distance of 7.4 Mb on average, and balanced founder contributions across EtNAM families. GWA analyses identified 11 genomic loci individually affecting up to 3 days in flowering time and more than 1.6 cm in height. We argue that the EtNAM is a powerful tool to support the production of new durum wheat varieties targeting local and global agriculture.
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Affiliation(s)
- Yosef G. Kidane
- Institute of Life SciencesScuola Superiore Sant'AnnaPisaItaly
- Bioversity InternationalAddis AbabaEthiopia
| | - Cherinet A. Gesesse
- Institute of Life SciencesScuola Superiore Sant'AnnaPisaItaly
- Amhara Regional Agricultural Research Institute (ARARI)Adet Agricultural Research CenterBahir DarEthiopia
| | | | - Ermias A. Desta
- Amhara Regional Agricultural Research Institute (ARARI)Adet Agricultural Research CenterBahir DarEthiopia
| | - Dejene K. Mengistu
- Institute of Life SciencesScuola Superiore Sant'AnnaPisaItaly
- Department of Dryland Crop and Horticultural SciencesMekelle UniversityMekelleEthiopia
| | | | - Mario Enrico Pè
- Institute of Life SciencesScuola Superiore Sant'AnnaPisaItaly
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90
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Abstract
The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses from extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.
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91
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Butrón A, Santiago R, Cao A, Samayoa LF, Malvar RA. QTLs for Resistance to Fusarium Ear Rot in a Multiparent Advanced Generation Intercross (MAGIC) Maize Population. PLANT DISEASE 2019; 103:897-904. [PMID: 30856072 DOI: 10.1094/pdis-09-18-1669-re] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Alternative approaches to linkage and association mapping using inbred panels may allow further insights into loci involved in resistance to Fusarium ear rot and lead to the discovery of suitable markers for breeding programs. Here, the suitability of a maize multiparent advanced-generation intercross population for detecting quantitative trait loci (QTLs) associated with Fusarium ear rot resistance was evaluated and found to be valuable in uncovering genomic regions containing resistance-associated loci in temperate materials. In total, 13 putative minor QTLs were located over all of the chromosomes, except chromosome 5, and frequencies of favorable alleles for resistance to Fusarium ear rot were, in general, high. These findings corroborated the quantitative characteristic of resistance to Fusarium ear rot in which many loci have small additive effects. Present and previous results indicate that crucial regions such as 210 to 220 Mb in chromosome 3 and 166 to 173 Mb in chromosome 7 (B73-RefGen-v2) contain QTLs for Fusarium ear rot resistance and fumonisin content.
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Affiliation(s)
- A Butrón
- 1 Misión Biológica de Galicia (CSIC), Box 28, Pontevedra 36080, Spain
| | - R Santiago
- 2 Facultad de Biología, Departamento de Biología Vegetal y Ciencias del Suelo, Universidad de Vigo, As Lagoas Marcosende, Vigo 36310, Spain
- 3 Agrobiología Ambiental, Calidad de Suelos y Plantas (UVIGO), Unidad Asociada a la MBG (CSIC), Pontevedra 36143, Spain; and
| | - A Cao
- 1 Misión Biológica de Galicia (CSIC), Box 28, Pontevedra 36080, Spain
| | - L F Samayoa
- 4 Department of Crop & Soil Sciences, North Carolina State University, Raleigh, NC 27695, U.S.A
| | - R A Malvar
- 1 Misión Biológica de Galicia (CSIC), Box 28, Pontevedra 36080, Spain
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92
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Septiani P, Lanubile A, Stagnati L, Busconi M, Nelissen H, Pè ME, Dell'Acqua M, Marocco A. Unravelling the genetic basis of Fusarium seedling rot resistance in the MAGIC maize population: novel targets for breeding. Sci Rep 2019; 9:5665. [PMID: 30952942 PMCID: PMC6451006 DOI: 10.1038/s41598-019-42248-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 03/26/2019] [Indexed: 12/16/2022] Open
Abstract
Fungal infection by Fusarium verticillioides is cause of prevalent maize disease leading to substantial reductions in yield and grain quality worldwide. Maize resistance to the fungus may occur at different developmental stages, from seedling to maturity. The breeding of resistant maize genotypes may take advantage of the identification of quantitative trait loci (QTL) responsible for disease resistance already commenced at seedling level. The Multi-parent Advance Generation Intercross (MAGIC) population was used to conduct high-definition QTL mapping for Fusarium seedling rot (FSR) resistance using rolled towel assay. Infection severity level, seedling weight and length were measured on 401 MAGIC maize recombinant inbred lines (RILs). QTL mapping was performed on reconstructed RIL haplotypes. One-fifth of the MAGIC RILs were resistant to FSR and 10 QTL were identified. For FSR, two QTL were detected at 2.8 Mb and 241.8 Mb on chromosome 4, and one QTL at 169.6 Mb on chromosome 5. Transcriptomic and sequencing information generated on the MAGIC founder lines was used to guide the identification of eight candidate genes within the identified FSR QTL. We conclude that the rolled towel assay applied to the MAGIC maize population provides a fast and cost-effective method to identify QTL and candidate genes for early resistance to F. verticillioides in maize.
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Affiliation(s)
- Popi Septiani
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Alessandra Lanubile
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, 29122, Italy
| | - Lorenzo Stagnati
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, 29122, Italy
| | - Matteo Busconi
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, 29122, Italy
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Centre for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Mario Enrico Pè
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Matteo Dell'Acqua
- Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Adriano Marocco
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, 29122, Italy.
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93
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Thyssen GN, Jenkins JN, McCarty JC, Zeng L, Campbell BT, Delhom CD, Islam MS, Li P, Jones DC, Condon BD, Fang DD. Whole genome sequencing of a MAGIC population identified genomic loci and candidate genes for major fiber quality traits in upland cotton (Gossypium hirsutum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:989-999. [PMID: 30506522 DOI: 10.1007/s00122-018-3254-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/27/2018] [Indexed: 05/25/2023]
Abstract
Significant associations between candidate genes and six major cotton fiber quality traits were identified in a MAGIC population using GWAS and whole genome sequencing. Upland cotton (Gossypium hirsutum L.) is the world's major renewable source of fibers for textiles. To identify causative genetic variants that influence the major agronomic measures of cotton fiber quality, which are used to set discount or premium prices on each bale of cotton in the USA, we measured six fiber phenotypes from twelve environments, across three locations and 7 years. Our 550 recombinant inbred lines were derived from a multi-parent advanced generation intercross population and were whole-genome-sequenced at 3× coverage, along with the eleven parental cultivars at 20× coverage. The segregation of 473,517 single nucleotide polymorphisms (SNPs) in this population, including 7506 non-synonymous mutations, was combined with phenotypic data to identify seven highly significant fiber quality loci. At these loci, we found fourteen genes with non-synonymous SNPs. Among these loci, some had simple additive effects, while others were only important in a subset of the population. We observed additive effects for elongation and micronaire, when the three most significant loci for each trait were examined. In an informative subset where the major multi-trait locus on chromosome A07:72-Mb was fixed, we unmasked the identity of another significant fiber strength locus in gene Gh_D13G1792 on chromosome D13. The micronaire phenotype only revealed one highly significant genetic locus at one environmental location, demonstrating a significant genetic by environment component. These loci and candidate causative variant alleles will be useful to cotton breeders for marker-assisted selection with minimal linkage drag and potential biotechnological applications.
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Affiliation(s)
- Gregory N Thyssen
- Cotton Fiber Bioscience Research Unit, USDA-ARS-SRRC, New Orleans, LA, 70124, USA
- Cotton Chemistry and Utilization Unit, USDA-ARS-SRRC, New Orleans, LA, 70124, USA
| | - Johnie N Jenkins
- Genetics and Sustainable Agriculture Research Unit, USDA-ARS, Mississippi State, MS, 39762, USA
| | - Jack C McCarty
- Genetics and Sustainable Agriculture Research Unit, USDA-ARS, Mississippi State, MS, 39762, USA
| | - Linghe Zeng
- Crop Genetics Research Unit, USDA-ARS, Stoneville, MS, 38776, USA
| | - B Todd Campbell
- Coastal Plain Soil, Water and Plant Conservation Research Unit, USDA-ARS, Florence, SC, 29501, USA
| | - Christopher D Delhom
- Cotton Structure and Quality Research Unit, USDA-ARS-SRRC, New Orleans, LA, 70124, USA
| | - Md Sariful Islam
- Sugarcane Production Research Unit, USDA-ARS, Canal Point, FL, 33438, USA
| | - Ping Li
- Cotton Fiber Bioscience Research Unit, USDA-ARS-SRRC, New Orleans, LA, 70124, USA
| | | | - Brian D Condon
- Cotton Chemistry and Utilization Unit, USDA-ARS-SRRC, New Orleans, LA, 70124, USA
| | - David D Fang
- Cotton Fiber Bioscience Research Unit, USDA-ARS-SRRC, New Orleans, LA, 70124, USA.
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94
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Wang X, Zhang R, Song W, Han L, Liu X, Sun X, Luo M, Chen K, Zhang Y, Yang H, Yang G, Zhao Y, Zhao J. Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV). Sci Rep 2019; 9:3458. [PMID: 30837510 PMCID: PMC6401315 DOI: 10.1038/s41598-019-39448-z] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 01/25/2019] [Indexed: 11/09/2022] Open
Abstract
Plant height (PH) is a key factor in maize (Zea mays L.) yield, biomass, and plant architecture. We investigated the PH of diverse maize inbred lines (117 temperate lines, 135 tropical lines) at four growth stages using unmanned aerial vehicle high-throughput phenotypic platforms (UAV-HTPPs). We extracted PH data using an automated pipeline based on crop surface models and orthomosaic model. The correlation between UAV and manually measured PH data reached 0.95. Under temperate field conditions, temperate maize lines grew faster than tropical maize lines at early growth stages, but tropical lines grew faster at later growth stages and ultimately became taller than temperate lines. A genome-wide association study identified 68 unique quantitative trait loci (QTLs) for seven PH-related traits, and 35% of the QTLs coincided with those previously reported to control PH. Generally, different QTLs controlled PH at different growth stages, but eight QTLs simultaneously controlled PH and growth rate at multiple growth stages. Based on gene annotations and expression profiles, we identified candidate genes controlling PH. The PH data collected by the UAV-HTPPs were credible and the genetic mapping power was high. Therefore, UAV-HTPPs have great potential for use in studies on PH.
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Affiliation(s)
- Xiaqing Wang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Ruyang Zhang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Wei Song
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Liang Han
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China
- College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, 037009, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuan Sun
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Meijie Luo
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Kuan Chen
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Yunxia Zhang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China
| | - Hao Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Guijun Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China
| | - Yanxin Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China.
| | - Jiuran Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture & Forestry Sciences, Beijing, 100097, China.
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95
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Stagnati L, Lanubile A, Samayoa LF, Bragalanti M, Giorni P, Busconi M, Holland JB, Marocco A. A Genome Wide Association Study Reveals Markers and Genes Associated with Resistance to Fusarium verticillioides Infection of Seedlings in a Maize Diversity Panel. G3 (BETHESDA, MD.) 2019; 9:571-579. [PMID: 30567831 PMCID: PMC6385986 DOI: 10.1534/g3.118.200916] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 12/18/2018] [Indexed: 12/12/2022]
Abstract
Fusarium verticillioides infects maize, causing ear rot, yield loss and contamination by fumonisin mycotoxins. The fungus can be transmitted via kernels and cause systemic infection in maize. Maize resistance to the fungus may occur at different developmental stages, from seedling to maturity. Resistance during kernel germination is part of the plant-pathogen interaction and so far this aspect has not been investigated. In the present study, a genome wide association study (GWAS) of resistance to Fusarium during the seedling developmental stage was conducted in a maize diversity panel using 226,446 SNP markers. Seedling germination and disease phenotypes were scored on artificially inoculated kernels using the rolled towel assay. GWAS identified 164 SNPs significantly associated with the traits examined. Four SNPs were associated with disease severity score after inoculation, 153 were associated with severity in asymptomatic kernels and 7 with the difference between the severity ratings in inoculated and non-inoculated seeds. A set of genes containing or physically near the significant SNPs were identified as candidates for Fusarium resistance at the seedling stage. Functional analysis revealed that many of these genes are directly involved in plant defense against pathogens and stress responses, including transcription factors, chitinase, cytochrome P450, and ubiquitination proteins. In addition, 25 genes were found in high linkage disequilibrium with the associated SNPs identified by GWAS and four of them directly involved in disease resistance. These findings contribute to understanding the complex system of maize-F. verticillioides and may improve genomic selection for Fusarium resistance at the seedling stage.
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Affiliation(s)
- Lorenzo Stagnati
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Alessandra Lanubile
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Luis F Samayoa
- Department of Crop & Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - Mario Bragalanti
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Paola Giorni
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - Matteo Busconi
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
| | - James B Holland
- Department of Crop & Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
- U.S. Department of Agriculture-Agricultural Research Service, Plant Science Research Unit, Raleigh, North Carolina 27695
| | - Adriano Marocco
- Dipartimento di Scienze delle Produzioni Vegetali Sostenibili, Università Cattolica del Sacro Cuore, via Emilia Parmense 84, 29122 Piacenza (Italy)
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96
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Fang C, Luo J. Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:91-100. [PMID: 30231195 DOI: 10.1111/tpj.14097] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/07/2018] [Accepted: 09/11/2018] [Indexed: 05/21/2023]
Abstract
Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.
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Affiliation(s)
- Chuanying Fang
- Hainan Key Laboratory for Sustainable Utilisation of Tropical Bioresource, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, 470228, China
| | - Jie Luo
- Hainan Key Laboratory for Sustainable Utilisation of Tropical Bioresource, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, 470228, China
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
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97
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Liu HJ, Yan J. Crop genome-wide association study: a harvest of biological relevance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:8-18. [PMID: 30368955 DOI: 10.1111/tpj.14139] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/13/2018] [Accepted: 10/22/2018] [Indexed: 05/20/2023]
Abstract
With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.
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Affiliation(s)
- Hai-Jun Liu
- 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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98
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R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations. Genetics 2018; 211:495-502. [PMID: 30591514 PMCID: PMC6366910 DOI: 10.1534/genetics.118.301595] [Citation(s) in RCA: 229] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 12/21/2018] [Indexed: 12/22/2022] Open
Abstract
R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multiparental populations, better handles modern high-dimensional data.... R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.
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99
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Stadlmeier M, Hartl L, Mohler V. Usefulness of a Multiparent Advanced Generation Intercross Population With a Greatly Reduced Mating Design for Genetic Studies in Winter Wheat. FRONTIERS IN PLANT SCIENCE 2018; 9:1825. [PMID: 30574161 PMCID: PMC6291512 DOI: 10.3389/fpls.2018.01825] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 11/23/2018] [Indexed: 05/05/2023]
Abstract
Multiparent advanced generation intercross (MAGIC) populations were recently developed to allow the high-resolution mapping of quantitative traits. We present a genetic linkage map of an elite but highly diverse eight-founder MAGIC population in common wheat (Triticum aestivum L.). Our MAGIC population is composed of 394 F6:8 recombinant inbred lines lacking significant signatures of population structure. The linkage map included 5435 SNP markers distributed over 2804 loci and spanning 5230 cM. The analysis of population parameters, including genetic structure, kinship, founder probabilities, and linkage disequilibrium and congruency to other maps indicated appropriate construction of both the population and the genetic map. It was shown that eight-founder MAGIC populations exhibit a greater number of loci and higher recombination rates, especially in the pericentromeric regions, compared to four-founder MAGIC, and biparental populations. In addition, our greatly simplified eight-parental MAGIC mating design with an additional eight-way intercross step was found to be equivalent to a MAGIC design with all 210 possible four-way crosses regarding the levels of missing founder assignments and the number of recombination events. Furthermore, the MAGIC population captured 71.7% of the allelic diversity available in the German wheat breeding gene pool. As a proof of principle, we demonstrated the application of the resource for quantitative trait loci mapping analyzing seedling resistance to powdery mildew. As wheat is a crop with many breeding objectives, this resource will allow scientists and breeders to carry out genetic studies for a wide range of breeder-relevant parameters in a single genetic background and reveal possible interactions between traits of economic importance.
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Affiliation(s)
- Melanie Stadlmeier
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Lorenz Hartl
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
| | - Volker Mohler
- Bavarian State Research Center for Agriculture, Institute for Crop Science and Plant Breeding, Freising, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
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Abstract
Climate change, associated with global warming, extreme weather events, and increasing incidence of weeds, pests and pathogens, is strongly influencing major cropping systems. In this challenging scenario, miscellaneous strategies are needed to expedite the rate of genetic gains with the purpose of developing novel varieties. Large plant breeding populations, efficient high-throughput technologies, big data management tools, and downstream biotechnology and molecular techniques are the pillars on which next generation breeding is based. In this review, we describe the toolbox the breeder has to face the challenges imposed by climate change, remark on the key role bioinformatics plays in the analysis and interpretation of big “omics” data, and acknowledge all the benefits that have been introduced into breeding strategies with the biotechnological and digital revolution.
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