1
|
Saini P, Sheikh I, Saini DK, Mir RR, Dhaliwal HS, Tyagi V. Consensus genomic regions associated with grain protein content in hexaploid and tetraploid wheat. Front Genet 2022; 13:1021180. [PMID: 36246648 PMCID: PMC9554612 DOI: 10.3389/fgene.2022.1021180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
A meta-analysis of QTLs associated with grain protein content (GPC) was conducted in hexaploid and tetraploid wheat to identify robust and stable meta-QTLs (MQTLs). For this purpose, as many as 459 GPC-related QTLs retrieved from 48 linkage-based QTL mapping studies were projected onto the newly developed wheat consensus map. The analysis resulted in the prediction of 57 MQTLs and 7 QTL hotspots located on all wheat chromosomes (except chromosomes 1D and 4D) and the average confidence interval reduced 2.71-fold in the MQTLs and QTL hotspots compared to the initial QTLs. The physical regions occupied by the MQTLs ranged from 140 bp to 224.02 Mb with an average of 15.2 Mb, whereas the physical regions occupied by QTL hotspots ranged from 1.81 Mb to 36.03 Mb with a mean of 8.82 Mb. Nineteen MQTLs and two QTL hotspots were also found to be co-localized with 45 significant SNPs identified in 16 previously published genome-wide association studies in wheat. Candidate gene (CG) investigation within some selected MQTLs led to the identification of 705 gene models which also included 96 high-confidence CGs showing significant expressions in different grain-related tissues and having probable roles in GPC regulation. These significantly expressed CGs mainly involved the genes/gene families encoding for the following proteins: aminotransferases, early nodulin 93, glutamine synthetases, invertase/pectin methylesterase inhibitors, protein BIG GRAIN 1-like, cytochrome P450, glycosyl transferases, hexokinases, small GTPases, UDP-glucuronosyl/UDP-glucosyltransferases, and EamA, SANT/Myb, GNAT, thioredoxin, phytocyanin, and homeobox domains containing proteins. Further, eight genes including GPC-B1, Glu-B1-1b, Glu-1By9, TaBiP1, GSr, TaNAC019-A, TaNAC019-D, and bZIP-TF SPA already known to be associated with GPC were also detected within some of the MQTL regions confirming the efficacy of MQTLs predicted during the current study.
Collapse
Affiliation(s)
- Pooja Saini
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| | - Imran Sheikh
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| | - Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punajb Agricultural University, Ludhiana, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture SKUAST-Kashmir, Srinagar, India
| | - Harcharan Singh Dhaliwal
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| | - Vikrant Tyagi
- Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, India
| |
Collapse
|
2
|
Hussain B, Akpınar BA, Alaux M, Algharib AM, Sehgal D, Ali Z, Aradottir GI, Batley J, Bellec A, Bentley AR, Cagirici HB, Cattivelli L, Choulet F, Cockram J, Desiderio F, Devaux P, Dogramaci M, Dorado G, Dreisigacker S, Edwards D, El-Hassouni K, Eversole K, Fahima T, Figueroa M, Gálvez S, Gill KS, Govta L, Gul A, Hensel G, Hernandez P, Crespo-Herrera LA, Ibrahim A, Kilian B, Korzun V, Krugman T, Li Y, Liu S, Mahmoud AF, Morgounov A, Muslu T, Naseer F, Ordon F, Paux E, Perovic D, Reddy GVP, Reif JC, Reynolds M, Roychowdhury R, Rudd J, Sen TZ, Sukumaran S, Ozdemir BS, Tiwari VK, Ullah N, Unver T, Yazar S, Appels R, Budak H. Capturing Wheat Phenotypes at the Genome Level. FRONTIERS IN PLANT SCIENCE 2022; 13:851079. [PMID: 35860541 PMCID: PMC9289626 DOI: 10.3389/fpls.2022.851079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.
Collapse
Affiliation(s)
- Babar Hussain
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | | | - Michael Alaux
- Université Paris-Saclay, INRAE, URGI, Versailles, France
| | - Ahmed M. Algharib
- Department of Environment and Bio-Agriculture, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zulfiqar Ali
- Institute of Plant Breeding and Biotechnology, MNS University of Agriculture, Multan, Pakistan
| | - Gudbjorg I. Aradottir
- Department of Pathology, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Arnaud Bellec
- French Plant Genomic Resource Center, INRAE-CNRGV, Castanet Tolosan, France
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Halise B. Cagirici
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Fred Choulet
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - James Cockram
- The John Bingham Laboratory, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Francesca Desiderio
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Pierre Devaux
- Research & Innovation, Florimond Desprez Group, Cappelle-en-Pévèle, France
| | - Munevver Dogramaci
- USDA, Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND, United States
| | - Gabriel Dorado
- Department of Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | | | - David Edwards
- University of Western Australia, Perth, WA, Australia
| | - Khaoula El-Hassouni
- State Plant Breeding Institute, The University of Hohenheim, Stuttgart, Germany
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium (IWGSC), Bethesda, MD, United States
| | - Tzion Fahima
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sergio Gálvez
- Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, Málaga, Spain
| | - Kulvinder S. Gill
- Department of Crop Science, Washington State University, Pullman, WA, United States
| | - Liubov Govta
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Alvina Gul
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Goetz Hensel
- Center of Plant Genome Engineering, Heinrich-Heine-Universität, Düsseldorf, Germany
- Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czechia
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | | | - Amir Ibrahim
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | | | | | - Tamar Krugman
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Yinghui Li
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Shuyu Liu
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Amer F. Mahmoud
- Department of Plant Pathology, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Alexey Morgounov
- Food and Agriculture Organization of the United Nations, Riyadh, Saudi Arabia
| | - Tugdem Muslu
- Molecular Biology, Genetics and Bioengineering, Sabanci University, Istanbul, Turkey
| | - Faiza Naseer
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Etienne Paux
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - Dragan Perovic
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Gadi V. P. Reddy
- USDA-Agricultural Research Service, Southern Insect Management Research Unit, Stoneville, MS, United States
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rajib Roychowdhury
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Jackie Rudd
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Taner Z. Sen
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | | | | | | | - Naimat Ullah
- Institute of Biological Sciences (IBS), Gomal University, D. I. Khan, Pakistan
| | - Turgay Unver
- Ficus Biotechnology, Ostim Teknopark, Ankara, Turkey
| | - Selami Yazar
- General Directorate of Research, Ministry of Agriculture, Ankara, Turkey
| | | | - Hikmet Budak
- Montana BioAgriculture, Inc., Missoula, MT, United States
| |
Collapse
|
3
|
Aoun M, Carter AH, Morris CF, Kiszonas AM. Genetic architecture of end-use quality traits in soft white winter wheat. BMC Genomics 2022; 23:440. [PMID: 35701755 PMCID: PMC9195237 DOI: 10.1186/s12864-022-08676-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
Background Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States. Results This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection. Conclusions In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08676-5.
Collapse
Affiliation(s)
- Meriem Aoun
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA.,Currently Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, OK, 74078, USA
| | - Arron H Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Craig F Morris
- USDA-ARS Western Wheat & Pulse Quality Laboratory, Washington State University, E-202 Food Quality Building, Pullman, WA, 99164, USA
| | - Alecia M Kiszonas
- USDA-ARS Western Wheat & Pulse Quality Laboratory, Washington State University, E-202 Food Quality Building, Pullman, WA, 99164, USA.
| |
Collapse
|
4
|
QTL mapping for quality traits using a high-density genetic map of wheat. PLoS One 2020; 15:e0230601. [PMID: 32208463 PMCID: PMC7092975 DOI: 10.1371/journal.pone.0230601] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 03/03/2020] [Indexed: 01/27/2023] Open
Abstract
Protein- and starch-related quality traits, which are quantitatively inherited and significantly influenced by the environment, are critical determinants of the end-use quality of wheat. We constructed a high-density genetic map containing 10,739 loci (5,399 unique loci) using a set of 184 recombinant inbred lines (RILs) derived from a cross of 'Tainong 18 × Linmai 6' (TL-RILs). In this study, a quantitative trait loci (QTLs) analysis was used to examine the genetic control of grain protein content, sedimentation value, farinograph parameters, falling number and the performance of the starch pasting properties using TL-RILs grown in a field for three years. A total of 106 QTLs for 13 quality traits were detected, distributed on the 21 chromosomes. Of these, 38 and 68 QTLs for protein- and starch-related traits, respectively, were detected in three environments and their average values (AV). Twenty-six relatively high-frequency QTLs (RHF-QTLs) that were detected in more than two environments. Twelve stable QTL clusters containing at least one RHF-QTL were detected and classified into three types: detected only for protein-related traits (type I), detected only for starch-related traits (type II), and detected for both protein- and starch-related traits (type III). A total of 339 markers flanked with 11 QTL clusters (all except C6), were found to be highly homologous with 282 high confidence (HC) and 57 low confidence (LC) candidate genes based on IWGSC RefSeq v 1.0. These stable QTLs and RHF-QTLs, especially those grouped into clusters, are credible and should be given priority for QTL fine-mapping and identification of candidate genes with which to explain the molecular mechanisms of quality development and inform marker-assisted breeding in the future.
Collapse
|
5
|
Ishikawa G, Hayashi T, Nakamura K, Tanaka T, Kobayashi F, Saito M, Ito H, Ikenaga S, Taniguchi Y, Nakamura T. Multifamily QTL analysis and comprehensive design of genotypes for high-quality soft wheat. PLoS One 2020; 15:e0230326. [PMID: 32160264 PMCID: PMC7065826 DOI: 10.1371/journal.pone.0230326] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 02/27/2020] [Indexed: 01/11/2023] Open
Abstract
Milling properties and flour color are essential selection criteria in soft wheat breeding. However, high phenotypic screening costs restrict selection to relatively few breeding lines in late generations. To achieve marker-based selection of these traits in early generations, we performed genetic dissection of quality traits using three doubled haploid populations that shared the high-quality soft wheat variety Kitahonami as the paternal parent. An amplicon sequencing approach allowed effective construction of well-saturated linkage maps of the populations. Marker-based heritability estimates revealed that target quality traits had relatively high values, indicating the possibility of selection in early generations. Taking advantage of Chinese Spring reference sequences, joint linkage maps of the three populations were generated. Based on the maps, multifamily quantitative trait locus (QTL) analysis revealed a total of 86 QTLs for ten traits investigated. In terms of target quality traits, 12 QTLs were detected for flour yield, and 12 were detected for flour redness (a* value). Among these QTLs, six for flour yield and nine for flour a* were segregating in more than two populations. Some relationships among traits were explained by QTL collocations on chromosomes, especially group 7 chromosomes. Ten different ideotypes with various combinations of favorable alleles for the flour yield and flour a* QTLs were generated. Phenotypes of derivatives from these ideotypes were predicted to design ideal genotypes for high-quality wheat. Simulations revealed the possibility of breeding varieties with better quality than Kitahonami.
Collapse
Affiliation(s)
- Goro Ishikawa
- Division of Basic Research, Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
- * E-mail:
| | - Takeshi Hayashi
- Division of Basic Research, Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Kazuhiro Nakamura
- Division of Lowland Farming Research, Kyusyu Okinawa Agricultural Research Center, National Agriculture and Food Research Organization, Chikugo, Fukuoka, Japan
| | - Tsuyoshi Tanaka
- Division of Basic Research, Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Fuminori Kobayashi
- Division of Basic Research, Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki, Japan
| | - Mika Saito
- Division of Field Crops and Horticulture Research, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization, Morioka, Iwate, Japan
| | - Hiroyuki Ito
- Division of Field Crops and Horticulture Research, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization, Morioka, Iwate, Japan
| | - Sachiko Ikenaga
- Division of Field Crops and Horticulture Research, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization, Morioka, Iwate, Japan
| | - Yoshinori Taniguchi
- Division of Field Crops and Horticulture Research, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization, Morioka, Iwate, Japan
| | - Toshiki Nakamura
- Division of Field Crops and Horticulture Research, Tohoku Agricultural Research Center, National Agriculture and Food Research Organization, Morioka, Iwate, Japan
| |
Collapse
|
6
|
Bernardo A, St. Amand P, Le HQ, Su Z, Bai G. Multiplex restriction amplicon sequencing: a novel next-generation sequencing-based marker platform for high-throughput genotyping. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:254-265. [PMID: 31199572 PMCID: PMC6920337 DOI: 10.1111/pbi.13192] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/04/2019] [Accepted: 06/11/2019] [Indexed: 05/31/2023]
Abstract
To enable rapid selection of traits in marker-assisted breeding, markers must be technically simple, low-cost, high-throughput and randomly distributed in a genome. We developed such a technology, designated as Multiplex Restriction Amplicon Sequencing (MRASeq), which reduces genome complexity by polymerase chain reaction (PCR) amplification of amplicons flanked by restriction sites. The first PCR primers contain restriction site sequences at 3'-ends, preceded by 6-10 bases of specific or degenerate nucleotide sequences and then by a unique M13-tail sequence which serves as a binding site for a second PCR that adds sequencing primers and barcodes to allow sample multiplexing for sequencing. The sequences of restriction sites and adjacent nucleotides can be altered to suit different species. Physical mapping of MRASeq SNPs from a biparental population of allohexaploid wheat (Triticum aestivum L.) showed a random distribution of SNPs across the genome. MRASeq generated thousands of SNPs from a wheat biparental population and natural populations of wheat and barley (Hordeum vulgare L.). This novel, next-generation sequencing-based genotyping platform can be used for linkage mapping to screen quantitative trait loci (QTL), background selection in breeding and many other genetics and breeding applications of various species.
Collapse
Affiliation(s)
- Amy Bernardo
- Department of Plant PathologyKansas State UniversityManhattanKSUSA
- Hard Winter Wheat Genetics Research UnitUSDA‐ARSManhattanKSUSA
| | - Paul St. Amand
- Hard Winter Wheat Genetics Research UnitUSDA‐ARSManhattanKSUSA
| | - Ha Quang Le
- Department of Plant PathologyKansas State UniversityManhattanKSUSA
| | - Zhenqi Su
- Department of AgronomyKansas State UniversityManhattanKSUSA
- China Agricultural UniversityBeijingChina
| | - Guihua Bai
- Hard Winter Wheat Genetics Research UnitUSDA‐ARSManhattanKSUSA
- Department of AgronomyKansas State UniversityManhattanKSUSA
| |
Collapse
|
7
|
Kumar A, Mantovani EE, Simsek S, Jain S, Elias EM, Mergoum M. Genome wide genetic dissection of wheat quality and yield related traits and their relationship with grain shape and size traits in an elite × non-adapted bread wheat cross. PLoS One 2019; 14:e0221826. [PMID: 31532783 PMCID: PMC6750600 DOI: 10.1371/journal.pone.0221826] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 08/15/2019] [Indexed: 12/21/2022] Open
Abstract
The genetic gain in yield and quality are two major targets of wheat breeding programs around the world. In this study, a high density genetic map consisting of 10,172 SNP markers identified a total of 43 genomic regions associated with three quality traits, three yield traits and two agronomic traits in hard red spring wheat (HRSW). When compared with six grain shape and size traits, the quality traits showed mostly independent genetic control (~18% common loci), while the yield traits showed moderate association (~53% common loci). Association of genomic regions for grain area (GA) and thousand-grain weight (TGW), with yield suggests that targeting an increase in GA may help enhancing wheat yield through an increase in TGW. Flour extraction (FE), although has a weak positive phenotypic association with grain shape and size, they do not share any common genetic loci. A major contributor to plant height was the Rht8 locus and the reduced height allele was associated with significant increase in grains per spike (GPS) and FE, and decrease in number of spikes per square meter and test weight. Stable loci were identified for almost all the traits. However, we could not find any QTL in the region of major known genes like GPC-B1, Ha, Rht-1, and Ppd-1. Epistasis also played an important role in the genetics of majority of the traits. In addition to enhancing our knowledge about the association of wheat quality and yield with grain shape and size, this study provides novel loci, genetic information and pre-breeding material (combining positive alleles from both parents) to enhance the cultivated gene pool in wheat germplasm. These resources are valuable in facilitating molecular breeding for wheat quality and yield improvement.
Collapse
Affiliation(s)
- Ajay Kumar
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States of America
| | - Eder E. Mantovani
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States of America
| | - Senay Simsek
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States of America
| | - Shalu Jain
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States of America
| | - Elias M. Elias
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States of America
| | - Mohamed Mergoum
- Department of Plant Sciences, North Dakota State University, Fargo, ND, United States of America
| |
Collapse
|
8
|
Abstract
Improving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.
Collapse
|
9
|
Ibba MI, Kiszonas AM, See DR, Skinner DZ, Morris CF. Mapping kernel texture in a soft durum (Triticum turgidum subsp. durum) wheat population. J Cereal Sci 2019. [DOI: 10.1016/j.jcs.2018.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
10
|
Boehm JD, Ibba MI, Kiszonas AM, See DR, Skinner DZ, Morris CF. Genetic analysis of kernel texture (grain hardness) in a hard red spring wheat (Triticum aestivum L.) bi-parental population. J Cereal Sci 2018. [DOI: 10.1016/j.jcs.2017.09.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
11
|
Jernigan KL, Godoy JV, Huang M, Zhou Y, Morris CF, Garland-Campbell KA, Zhang Z, Carter AH. Genetic Dissection of End-Use Quality Traits in Adapted Soft White Winter Wheat. FRONTIERS IN PLANT SCIENCE 2018; 9:271. [PMID: 29593752 PMCID: PMC5861628 DOI: 10.3389/fpls.2018.00271] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 02/16/2018] [Indexed: 05/19/2023]
Abstract
Soft white wheat is used in domestic and foreign markets for various end products requiring specific quality profiles. Phenotyping for end-use quality traits can be costly, time-consuming and destructive in nature, so it is advantageous to use molecular markers to select experimental lines with superior traits. An association mapping panel of 469 soft white winter wheat cultivars and advanced generation breeding lines was developed from regional breeding programs in the U.S. Pacific Northwest. This panel was genotyped on a wheat-specific 90 K iSelect single nucleotide polymorphism (SNP) chip. A total of 15,229 high quality SNPs were selected and combined with best linear unbiased predictions (BLUPs) from historical phenotypic data of the genotypes in the panel. Genome-wide association mapping was conducted using the Fixed and random model Circulating Probability Unification (FarmCPU). A total of 105 significant marker-trait associations were detected across 19 chromosomes. Potentially new loci for total flour yield, lactic acid solvent retention capacity, flour sodium dodecyl sulfate sedimentation and flour swelling volume were also detected. Better understanding of the genetic factors impacting end-use quality enable breeders to more effectively discard poor quality germplasm and increase frequencies of favorable end-use quality alleles in their breeding populations.
Collapse
Affiliation(s)
- Kendra L. Jernigan
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Jayfred V. Godoy
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Meng Huang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Yao Zhou
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Craig F. Morris
- Western Wheat Quality Laboratory, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States
- Wheat Health, Genetics, and Quality Research Unit, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States
| | - Kimberly A. Garland-Campbell
- Wheat Health, Genetics, and Quality Research Unit, Agricultural Research Service, United States Department of Agriculture, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Arron H. Carter
| |
Collapse
|