High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat
Key message High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fi...
Gespeichert in:
| Veröffentlicht in: | Theoretical and applied genetics Jg. 134; H. 9; S. 2857 - 2873 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2021
Springer Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0040-5752, 1432-2242, 1432-2242 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Key message
High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding.
Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat. |
|---|---|
| AbstractList | Key message
High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding.
Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat. KEY MESSAGE: High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat. Key message High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to [less than or equal to] 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat. High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat.KEY MESSAGEHigh-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat. Key message Key messageHigh-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding.Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat. |
| Audience | Academic |
| Author | Zhao, Meng St. Amand, Paul Kong, Lingrang Yan, Qiang Dong, Lei Wu, Jiajie He, Fang Liu, Chunxia Jiang, Hongming Liu, Shubing Liang, Yunlong Wang, Liming Li, Anfei Wang, Danfeng Lu, Yue Bernardo, Amy Yuan, Xiufang Su, Yu Bai, Guihua Wu, Yuye Li, Linzhi Pang, Yunlong Zhang, Huirui Li, Wenhui |
| Author_xml | – sequence: 1 givenname: Yunlong surname: Pang fullname: Pang, Yunlong organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 2 givenname: Yuye surname: Wu fullname: Wu, Yuye organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 3 givenname: Chunxia surname: Liu fullname: Liu, Chunxia organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 4 givenname: Wenhui surname: Li fullname: Li, Wenhui organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 5 givenname: Paul surname: St. Amand fullname: St. Amand, Paul organization: Hard Winter Wheat Genetics Research Unit – sequence: 6 givenname: Amy surname: Bernardo fullname: Bernardo, Amy organization: Hard Winter Wheat Genetics Research Unit, Department of Plant Pathology, Kansas State University – sequence: 7 givenname: Danfeng surname: Wang fullname: Wang, Danfeng organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 8 givenname: Lei surname: Dong fullname: Dong, Lei organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 9 givenname: Xiufang surname: Yuan fullname: Yuan, Xiufang organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 10 givenname: Huirui surname: Zhang fullname: Zhang, Huirui organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 11 givenname: Meng surname: Zhao fullname: Zhao, Meng organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 12 givenname: Linzhi surname: Li fullname: Li, Linzhi organization: Yantai Academy of Agricultural Sciences – sequence: 13 givenname: Liming surname: Wang fullname: Wang, Liming organization: College of Agriculture, Henan University of Science and Technology – sequence: 14 givenname: Fang surname: He fullname: He, Fang organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, College of Agriculture, Guizhou University – sequence: 15 givenname: Yunlong surname: Liang fullname: Liang, Yunlong organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 16 givenname: Qiang surname: Yan fullname: Yan, Qiang organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 17 givenname: Yue surname: Lu fullname: Lu, Yue organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 18 givenname: Yu surname: Su fullname: Su, Yu organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 19 givenname: Hongming surname: Jiang fullname: Jiang, Hongming organization: Yantai Academy of Agricultural Sciences – sequence: 20 givenname: Jiajie surname: Wu fullname: Wu, Jiajie organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 21 givenname: Anfei surname: Li fullname: Li, Anfei organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 22 givenname: Lingrang surname: Kong fullname: Kong, Lingrang organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University – sequence: 23 givenname: Guihua surname: Bai fullname: Bai, Guihua organization: Hard Winter Wheat Genetics Research Unit, Department of Plant Pathology, Kansas State University – sequence: 24 givenname: Shubing orcidid: 0000-0001-6450-0459 surname: Liu fullname: Liu, Shubing email: sbliu@sdau.edu.cn organization: State Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University |
| BookMark | eNqFkk1r3DAQhk1JoZu0f6AnQy_tQeno0_YxhLYJBAr9OAutPHYUvNJWktnm31exC2FDSdFBMPO8M9LMe1qd-OCxqt5SOKcAzccEQBkjwCgB3ipO1ItqQwVnhDHBTqoNgAAiG8leVacp3QEAk8A31eHKjbckYgrTnF3w9Yg-7JAcXI-1SSlYZ5Z4ynN_Xxvfr4Sz9T5i7-ySHEKse5fQJKxLLZey8RYX2oapr3OYMC4h5-vDLZr8uno5mCnhm7_3WfXz86cfl1fk5uuX68uLG2JFqzJpmWIwdMoo2wvDO9sryQFtB1tpqJSG94MsGbmVtuHUKLU1ZgAqoFCs2fKz6v1adx_DrxlT1juXLE6T8RjmpJniSrad6pr_o5IroQQHWdB3T9C7MEdfPlIo2XWScyEeqdFMqJ0fQo7GPhTVF6qhktOym0Kd_4Mqp8cy5bLmwZX4keDDkaAwGX_n0cwp6evv345ZtrI2hpQiDnof3c7Ee01BPzhHr87RxTl6cY5WRdQ-EVmXFxeUl7npeSlfpan08SPGx8E8o_oD-lXX-w |
| CitedBy_id | crossref_primary_10_3389_fgene_2024_1473717 crossref_primary_10_1007_s00122_023_04352_8 crossref_primary_10_3390_ijms232214099 crossref_primary_10_1007_s11033_022_07287_3 crossref_primary_10_1007_s00122_024_04689_8 crossref_primary_10_3390_ijms24010006 crossref_primary_10_3390_plants12234014 crossref_primary_10_1016_j_jia_2025_02_028 crossref_primary_10_3390_agriculture13030642 crossref_primary_10_1016_j_plaphe_2025_100061 crossref_primary_10_1002_advs_202412423 crossref_primary_10_1007_s00122_024_04768_w crossref_primary_10_1007_s00604_024_06778_3 crossref_primary_10_3390_agronomy13041173 crossref_primary_10_3390_genes14071447 crossref_primary_10_3389_fgene_2024_1487700 crossref_primary_10_1016_j_jenvman_2024_123461 crossref_primary_10_1007_s00425_022_03843_0 crossref_primary_10_3390_plants12223864 |
| Cites_doi | 10.1093/gigascience/gix097 10.7717/peerj.8190 10.1186/1753-6561-5-S3-S12 10.1007/978-3-319-23162-4_13 10.1093/genetics/157.4.1819 10.1371/journal.pone.0150717 10.1111/jipb.12918 10.1038/nbt.3877 10.1094/PHYTO-07-17-0254-R 10.1126/science.1166453 10.1007/s00122-014-2378-8 10.1038/s41467-020-18738-5 10.1534/g3.116.037622 10.1007/s10681-011-0361-x 10.1023/A:1003950431049 10.3835/plantgenome2011.08.0024 10.3835/plantgenome2016.01.0005 10.1126/science.aar7191 10.1038/s41477-021-00869-2 10.1111/nph.16761 10.1007/s00122-017-2946-9 10.1007/s12298-017-0495-y 10.1007/s00122-009-1031-4 10.1007/s00122-018-3135-1 10.1016/j.molp.2018.02.013 10.1016/S2095-3119(16)61610-6 10.1094/PDIS-02-17-0168-RE 10.1094/PD-90-0980 10.1093/bfgp/elq001 10.3835/plantgenome2019.05.0033 10.1038/s41467-020-15139-6 10.1016/j.tplants.2014.05.006 10.1007/s00122-016-2694-2 10.1002/ps.3767 10.1186/s13059-020-02225-7 10.1111/tpj.13424 10.1111/j.1467-7652.2010.00536.x 10.1111/nph.15696 10.1016/j.molp.2020.07.008 10.1038/s41586-020-2961-x 10.1111/jipb.12657 10.1016/j.funbio.2020.02.013 10.1007/978-3-319-24277-4 10.1186/1471-2164-15-898 10.3389/fpls.2017.01914 10.1038/s41467-019-11872-9 10.1007/s00122-017-2897-1 10.1093/pcp/pct164 10.1111/nph.14159 10.1094/PHYTO-06-18-0208-RVW 10.1093/bioinformatics/btm308 10.1111/mpp.12618 10.1016/j.molp.2018.03.004 10.1016/j.agrformet.2018.06.006 10.1093/bioinformatics/bts444 10.1111/pbi.13452 10.1007/s00122-015-2642-6 10.1016/S2095-3119(16)61379-5 10.1111/ppa.13166 10.1038/ng.3439 10.1094/PDIS-08-19-1745-RE 10.1126/science.1166289 10.1007/s11103-007-9201-8 10.1016/j.plantsci.2015.08.021 10.1007/s00122-014-2341-8 10.1038/s41467-020-14294-0 10.1007/s00122-014-2445-1 10.1111/tpj.14150 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 COPYRIGHT 2021 Springer The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 – notice: COPYRIGHT 2021 Springer – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. – notice: 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
| DBID | AAYXX CITATION ISR 3V. 7SS 7TK 7X7 7XB 88A 88E 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS RC3 7X8 7S9 L.6 |
| DOI | 10.1007/s00122-021-03863-6 |
| DatabaseName | CrossRef Gale In Context: Science ProQuest Central (Corporate) Entomology Abstracts (Full archive) Neurosciences Abstracts Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database ProQuest Biological Science Database Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA MEDLINE - Academic ProQuest Central Student |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture Biology |
| EISSN | 1432-2242 |
| EndPage | 2873 |
| ExternalDocumentID | A671531242 10_1007_s00122_021_03863_6 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: Shandong Province Agricultural Fine Seeds Project grantid: 2019LZGC015, 2016LZGC023 – fundername: National Key Research and Development Programs of China grantid: 2016YFD0101802, 2016ZX08009003-001-006 |
| GroupedDBID | --- -4W -56 -5G -BR -DZ -EM -Y2 -~C -~X .86 .VR 06C 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29Q 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3SX 3V. 4.4 406 408 409 40D 40E 53G 5QI 5VS 67N 67Z 6NX 78A 7X7 88A 88E 8AO 8FE 8FH 8FI 8FJ 8UJ 95- 95. 95~ 96X A8Z AAAVM AABHQ AACDK AAHBH AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADYPR ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ AVWKF AXYYD AZFZN B-. BA0 BBNVY BBWZM BDATZ BENPR BGNMA BHPHI BPHCQ BSONS BVXVI CAG CCPQU COF CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBD EBLON EBS EIOEI EJD EMB EMOBN EN4 EPAXT ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IFM IHE IHR IJ- IKXTQ INH INR ISR ITC ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW KPH LAS LK8 LLZTM M0L M1P M4Y M7P MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P0- P19 PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RIG RNI ROL RPX RRX RSV RZK S16 S1Z S26 S27 S28 S3A S3B SAP SBL SBY SCLPG SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZN T13 T16 TSG TSK TSV TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK WK6 WK8 Y6R YLTOR Z45 Z7S Z7U Z7V Z7W Z7Y Z83 Z85 Z87 Z8N Z8O Z8P Z8Q Z8S Z8W Z8Z Z91 ZMTXR ZOVNA ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB 7SS 7TK 7XB 8FD 8FK AZQEC DWQXO ESTFP FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS RC3 7X8 PUEGO 7S9 L.6 |
| ID | FETCH-LOGICAL-c486t-82620f96a6cd4a39cd6530ec90b5a155a3df5d4a5b5c731a66baaf014030e27b3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 21 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000656785900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0040-5752 1432-2242 |
| IngestDate | Fri Sep 05 14:31:49 EDT 2025 Sun Sep 28 12:07:13 EDT 2025 Wed Nov 05 01:08:41 EST 2025 Sat Nov 29 13:13:45 EST 2025 Sat Nov 29 10:04:35 EST 2025 Wed Nov 26 09:24:06 EST 2025 Tue Nov 18 20:26:19 EST 2025 Sat Nov 29 06:46:40 EST 2025 Fri Feb 21 02:47:44 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c486t-82620f96a6cd4a39cd6530ec90b5a155a3df5d4a5b5c731a66baaf014030e27b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-6450-0459 |
| PQID | 2559953344 |
| PQPubID | 54040 |
| PageCount | 17 |
| ParticipantIDs | proquest_miscellaneous_2636589697 proquest_miscellaneous_2536464305 proquest_journals_2559953344 gale_infotracmisc_A671531242 gale_infotracacademiconefile_A671531242 gale_incontextgauss_ISR_A671531242 crossref_primary_10_1007_s00122_021_03863_6 crossref_citationtrail_10_1007_s00122_021_03863_6 springer_journals_10_1007_s00122_021_03863_6 |
| PublicationCentury | 2000 |
| PublicationDate | 20210900 2021-09-00 20210901 |
| PublicationDateYYYYMMDD | 2021-09-01 |
| PublicationDate_xml | – month: 9 year: 2021 text: 20210900 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationSubtitle | International Journal of Plant Breeding Research |
| PublicationTitle | Theoretical and applied genetics |
| PublicationTitleAbbrev | Theor Appl Genet |
| PublicationYear | 2021 |
| Publisher | Springer Berlin Heidelberg Springer Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer – name: Springer Nature B.V |
| References | Oliver (CR43) 2014; 70 Sheng (CR53) 1988; 1 Li, Dong, Li, Wang, Xie, Qiu, Li, Shi, Yang, Wu, Chen, Lu, Guo, Zhang, Zhang, Zhu, Li, Zhang, Wang, Yuan, Liu, Yu, Luo, Fahima, Nevo, Li, Liu (CR34) 2020; 228 (CR25) 2018; 361 Liu, Shi, Yang (CR37) 2018; 60 Schulz-Streeck, Ogutu, Piepho (CR51) 2011; 5 Borrill, Harrington, Uauy (CR4) 2019; 97 Endelman (CR12) 2011; 4 Jia, Xie, Cheng, Kong, Wang, Gao, Zhao, Guo, Wang, Li, Cui, Hu, Zhao, Wang, Ru, Zhang (CR27) 2021; 22 Sapkota, Hao, Johnson, Buck, Aoun, Mergoum (CR49) 2019; 12 Tang, Zhao, Ren, Yang, Zhu, Zhao (CR56) 2020; 62 Afzal, Chaudhari, Gul, Farooq, Ali, Nisar, Sarfraz, Shehzadi, Mujeeb-Kazi, Hakeem (CR1) 2015 Dong, Hegarty, Zhang, Zhang, Chao, Chen, Zhou, Dubcovsky (CR11) 2017; 130 Pinto da Silva, Zanella, Martinelli, Chaves, Hiebert, McCallum, Boyd (CR45) 2018; 108 Michel, Ametz, Gungor, Epure, Grausgruber, Löschenberger, Buerstmayr (CR41) 2016; 129 Walkowiak, Gao, Monat, Haberer, Kassa, Brinton, Ramirez-Gonzalez, Kolodziej, Delorean, Thambugala, Klymiuk, Byrns, Gundlach, Bandi, Siri, Nilsen, Aquino, Himmelbach, Copetti, Ban, Venturini, Bevan, Clavijo, Koo, Ens, Wiebe, N’Diaye, Fritz, Gutwin, Fiebig, Fosker, Fu, Accinelli, Gardner, Fradgley, Gutierrez-Gonzalez, Halstead-Nussloch, Hatakeyama, Koh, Deek, Costamagna, Fobert, Heavens, Kanamori, Kawaura, Kobayashi, Krasileva, Kuo, McKenzie, Murata, Nabeka, Paape, Padmarasu, Percival-Alwyn, Kagale, Scholz, Sese, Juliana, Singh, Shimizu-Inatsugi, Swarbreck, Cockram, Budak, Tameshige, Tanaka, Tsuji, Wright, Wu, Steuernagel, Small, Cloutier, Keeble-Gagnère, Muehlbauer, Tibbets, Nasuda, Melonek, Hucl, Sharpe, Clark, Legg, Bharti, Langridge, Hall, Uauy, Mascher, Krattinger, Handa, Shimizu, Distelfeld, Chalmers, Keller, Mayer, Poland, Stein, McCartney, Spannagl, Wicker, Pozniak (CR58) 2020; 588 Liu, Liu, Zhang, Jia, Wang, Gao, Peng, Jin, Chen (CR36) 2017; 47 Ding, Shi, Yang (CR10) 2019; 222 Krattinger, Lagudah, Spielmeyer, Singh, Huerta-Espino, McFadden, Bossolini, Selter, Keller (CR31) 2009; 323 Huerta-Espino, Singh, Germán, McCallum, Park, Chen, Bhardwaj, Goyeau (CR24) 2011; 179 Winfield, Lu, Wilson, Coghill, Edwards (CR61) 2010; 8 Fu, Uauy, Distelfeld, Blechl, Epstein, Chen, Sela, Fahima, Dubcovsky (CR14) 2009; 323 He, Zhu, Zhao, Jiang, Ji, Ji, Qiu, Li, Bie (CR22) 2018; 11 Kang, Zhou, Merry, Barry (CR29) 2020; 69 Bassi, Bentley, Charmet, Ortiz, Crossa (CR2) 2016; 242 Guo, Ren, Tang, Shi, Zhou (CR16) 2019; 7 Juliana, Singh, Singh, Crossa, Huerta-Espino, Lan, Bhavani, Rutkoski, Poland, Bergstrom, Sorrells (CR28) 2017; 130 Zhang, Hiebert, McIntosh, McCallum, Thomas, Hoxha, Singh, Bansal (CR70) 2016; 129 Sánchez-Martín, Widrig, Herren, Wicker, Zbinden, Gronnier, Spörri, Praz, Heuberger, Kolodziej, Isaksson, Steuernagel, Karafiátová, Doležel, Zipfel, Keller (CR48) 2021; 7 Xu, Feng, Fan, Liu, Li, Zhou, Ma (CR66) 2018; 17 Zhang, Yang, Wang, Liu, Li, Fu, Wang, Nie, Liu, Ji (CR69) 2014; 15 Zhang, Huang, Zhang, Hao, Lyu, Wang, Epstein, Liu, Kou, Qi, Chen, Li, Gao, Ni, Zhang, Hao, Wang, Chen, Luo, Zheng, Wu, Liu, Fu (CR71) 2019; 10 Rong, Millet, Manisterski, Feldman (CR47) 2000; 115 Daetwyler, Bansal, Bariana, Hayden, Hayes (CR8) 2014; 127 Cheng, Xu, Wang, See, Chen (CR6) 2014; 127 Kolmer, Bernardo, Bai, Hayden, Chao (CR30) 2018; 108 Prasad, Savadi, Bhardwaj, Gupta (CR46) 2020; 124 Desta, Ortiz (CR9) 2014; 19 Moore, Herrera-Foessel, Lan, Schnippenkoetter, Ayliffe, Huerta-Espino, Lillemo, Viccars, Milne, Periyannan, Kong, Spielmeyer, Talbot, Bariana, Patrick, Dodds, Singh, Lagudah (CR42) 2015; 47 Cloutier, McCallum, Loutre, Banks, Wicker, Feuillet, Keller, Jordan (CR7) 2007; 65 Lu, Guo, Wang, Li, Li, Li, Qiu, Shi, Yang, Wang, Guo, Xie, Wu, Chen, Li, Zhang, Dong, Zhang, Zhu, Yu, Zhang, Deal, Huo, Liu, Luo, Dvorak, Gu, Li, Liu (CR39) 2020; 11 Battenfield, Guzmán, Gaynor, Singh, Peña, Dreisigacker, Fritz, Poland (CR3) 2016 Li, Shi, Hu, Wu, Qiu, Qu, Xie, Wu, Zhang, Yang, Liu, Zhou, Liu, Li (CR33) 2020; 104 Zeng, Luo (CR68) 2006; 90 Thind, Wicker, Šimková, Fossati, Moullet, Brabant, Vrána, Doležel, Krattinger (CR57) 2017; 35 Kruse, Carle, Wen, Skinner, Murray, Garland-Campbell, Carter (CR32) 2017; 7 Bradbury, Zhang, Kroon, Casstevens, Ramdoss, Buckler (CR5) 2007; 23 Lu, Chen, Liu, He, Xia (CR38) 2016; 15 Wickham (CR60) 2016 Zimin, Puiu, Hall, Kingan, Clavijo, Salzberg (CR72) 2017; 6 Jannink, Lorenz, Iwata (CR26) 2010; 9 Graham, McNeney, Blay, Shin (CR15) 2006; 16 Xiao, Liu, Asseng, Xia, Tang, Liu, Cao, Zhu (CR64) 2018; 260–261 Guo, Xin, Wang, Yao, Hu, Song, Yu, Chen, Wang, Guan, Appels, Peng, Ni, Sun (CR17) 2020; 11 Sun, Hu, Song, Qiu, Cui, Wu, Zhang, Liu, Yang, Qu, Li, Li, Cheng, Zhou, Liu, Li, Li (CR54) 2018; 131 Meuwissen, Hayes, Goddard (CR40) 2001; 157 Xing, Hu, Liu, Witek, Zhou, Xu, Zhou, Gao, Huang, Zhang, Wang, Chen, Wang, Jones, Karafiátová, Vrána, Bartoš, Doležel, Tian, Wu, Cao (CR65) 2018; 11 Wu, Yu, Wang, Ce, Huang, Jiao, Yu, Nie, Wang, Liu, Weining, Singh, Bhavani, Kang, Han, Zeng (CR62) 2020 Sasaki, Christov, Tsuda, Imai (CR50) 2013; 55 Tang, Cao, Xu, Jiang, Luo, Ma, Fan, Zhou (CR55) 2017; 101 Pang, Liu, Wang, St Amand, Bernardo, Li, He, Li, Wang, Yuan, Dong, Su, Zhang, Zhao, Liang, Jia, Shen, Lu, Jiang, Wu, Li, Wang, Kong, Bai, Liu (CR44) 2020; 13 He, Ji, Zhu, Li, Zhao, Jiang, Bie (CR21) 2017 Hao, Wang, Wang, Wang, Fu, Huang, Kang (CR19) 2016; 11 Würschum, Longin, Hahn, Tucker, Leiser (CR63) 2017; 89 Lipka, Tian, Wang, Peiffer, Li, Bradbury, Gore, Buckler, Zhang (CR35) 2012; 28 Figueroa, Hammond-Kosack, Solomon (CR13) 2018; 19 CR20 Hua, Liu, Zhu, Xie, Yang, Zhou, Duan, Sun, Liu (CR23) 2009; 119 Wang, Zou, Li, Lin, Tang (CR59) 2020; 11 Jin, Zhai, Wang, Ding, Guo, Bai, Wang (CR67) 2018; 24 Schwessinger (CR52) 2017; 213 Hao, Parks, Cowger, Chen, Wang, Bland, Murphy, Guedira, Brown-Guedira, Johnson (CR18) 2015; 128 Z Dong (3863_CR11) 2017; 130 B Sheng (3863_CR53) 1988; 1 J Huerta-Espino (3863_CR24) 2011; 179 S Walkowiak (3863_CR58) 2020; 588 X-d Xu (3863_CR66) 2018; 17 Y Kang (3863_CR29) 2020; 69 M Li (3863_CR34) 2020; 228 J Sánchez-Martín (3863_CR48) 2021; 7 J Wu (3863_CR62) 2020 IWGSC (3863_CR25) 2018; 361 J Liu (3863_CR37) 2018; 60 THE Meuwissen (3863_CR40) 2001; 157 L Xiao (3863_CR64) 2018; 260–261 Y Li (3863_CR33) 2020; 104 J-l Lu (3863_CR38) 2016; 15 HD Daetwyler (3863_CR8) 2014; 127 P Prasad (3863_CR46) 2020; 124 Y Pang (3863_CR44) 2020; 13 F Afzal (3863_CR1) 2015 EB Kruse (3863_CR32) 2017; 7 J Guo (3863_CR16) 2019; 7 K Tang (3863_CR56) 2020; 62 L Xing (3863_CR65) 2018; 11 H Zhang (3863_CR69) 2014; 15 Y Hao (3863_CR19) 2016; 11 Y Ding (3863_CR10) 2019; 222 D Fu (3863_CR14) 2009; 323 X Tang (3863_CR55) 2017; 101 SD Battenfield (3863_CR3) 2016 P Borrill (3863_CR4) 2019; 97 S Sapkota (3863_CR49) 2019; 12 M Figueroa (3863_CR13) 2018; 19 P Juliana (3863_CR28) 2017; 130 P Lu (3863_CR39) 2020; 11 S Cloutier (3863_CR7) 2007; 65 JW Moore (3863_CR42) 2015; 47 P Zhang (3863_CR70) 2016; 129 P Cheng (3863_CR6) 2014; 127 S-M Zeng (3863_CR68) 2006; 90 JK Rong (3863_CR47) 2000; 115 FM Bassi (3863_CR2) 2016; 242 H He (3863_CR22) 2018; 11 H Wang (3863_CR59) 2020; 11 AK Thind (3863_CR57) 2017; 35 Y Jin (3863_CR67) 2018; 24 J Graham (3863_CR15) 2006; 16 T Schulz-Streeck (3863_CR51) 2011; 5 W Guo (3863_CR17) 2020; 11 AV Zimin (3863_CR72) 2017; 6 H Sun (3863_CR54) 2018; 131 PJ Bradbury (3863_CR5) 2007; 23 B Liu (3863_CR36) 2017; 47 W Hua (3863_CR23) 2009; 119 B Schwessinger (3863_CR52) 2017; 213 J Jia (3863_CR27) 2021; 22 JL Jannink (3863_CR26) 2010; 9 S Michel (3863_CR41) 2016; 129 ZA Desta (3863_CR9) 2014; 19 H He (3863_CR21) 2017 RP Oliver (3863_CR43) 2014; 70 H Wickham (3863_CR60) 2016 AE Lipka (3863_CR35) 2012; 28 K Sasaki (3863_CR50) 2013; 55 MO Winfield (3863_CR61) 2010; 8 T Würschum (3863_CR63) 2017; 89 S Krattinger (3863_CR31) 2009; 323 GB Pinto da Silva (3863_CR45) 2018; 108 JB Endelman (3863_CR12) 2011; 4 Y Hao (3863_CR18) 2015; 128 JA Kolmer (3863_CR30) 2018; 108 C Zhang (3863_CR71) 2019; 10 3863_CR20 |
| References_xml | – volume: 6 start-page: 1 year: 2017 end-page: 7 ident: CR72 article-title: The first near-complete assembly of the hexaploid bread wheat genome, publication-title: Gigascience doi: 10.1093/gigascience/gix097 – volume: 7 start-page: e8190 year: 2019 end-page: e8190 ident: CR16 article-title: Characterization and expression profiling of the ICE-CBF-COR genes in wheat publication-title: PeerJ doi: 10.7717/peerj.8190 – volume: 5 start-page: S12 year: 2011 ident: CR51 article-title: Pre-selection of markers for genomic selection publication-title: BMC Proceed doi: 10.1186/1753-6561-5-S3-S12 – start-page: 293 year: 2015 end-page: 317 ident: CR1 article-title: Bread wheat ( L.) under biotic and abiotic stresses: an overview publication-title: Crop production and global environmental issues doi: 10.1007/978-3-319-23162-4_13 – volume: 157 start-page: 1819 year: 2001 end-page: 1829 ident: CR40 article-title: Prediction of total genetic value using genome-wide dense marker maps publication-title: Genetics doi: 10.1093/genetics/157.4.1819 – volume: 11 start-page: e0150717 year: 2016 ident: CR19 article-title: Transcriptome analysis provides insights into the mechanisms underlying wheat plant resistance to stripe rust at the adult plant stage publication-title: PLoS ONE doi: 10.1371/journal.pone.0150717 – volume: 62 start-page: 258 year: 2020 end-page: 263 ident: CR56 article-title: The transcription factor ICE1 functions in cold stress response by binding to the promoters of CBF and COR genes publication-title: J Integr Plant Biol doi: 10.1111/jipb.12918 – volume: 35 start-page: 793 year: 2017 end-page: 796 ident: CR57 article-title: Rapid cloning of genes in hexaploid wheat using cultivar-specific long-range chromosome assembly publication-title: Nat Biotechnol doi: 10.1038/nbt.3877 – volume: 108 start-page: 246 year: 2018 end-page: 253 ident: CR30 article-title: Adult plant leaf rust resistance derived from toropi wheat is conditioned by and three minor QTL publication-title: Phytopathology doi: 10.1094/PHYTO-07-17-0254-R – volume: 323 start-page: 1360 year: 2009 end-page: 1363 ident: CR31 article-title: A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat publication-title: Science doi: 10.1126/science.1166453 – volume: 127 start-page: 2267 year: 2014 end-page: 2277 ident: CR6 article-title: Molecular mapping of genes and for stripe rust resistance in hexaploid derivatives of durum wheat accessions PI 331260 and PI 480016 publication-title: Theor Appl Genet doi: 10.1007/s00122-014-2378-8 – volume: 11 start-page: 5085 year: 2020 ident: CR17 article-title: Origin and adaptation to high altitude of Tibetan semi-wild wheat publication-title: Nat Commun doi: 10.1038/s41467-020-18738-5 – volume: 7 start-page: 775 issue: 3 year: 2017 end-page: 780 ident: CR32 article-title: Genomic regions associated with tolerance to freezing stress and snow mold in winter wheat publication-title: G3-Genes Genom Genet doi: 10.1534/g3.116.037622 – volume: 179 start-page: 143 year: 2011 end-page: 160 ident: CR24 article-title: Global status of wheat leaf rust caused by Puccinia triticina publication-title: Euphytica doi: 10.1007/s10681-011-0361-x – volume: 115 start-page: 121 year: 2000 end-page: 126 ident: CR47 article-title: A new powdery mildew resistance gene: introgression from wild emmer into common wheat and RFLP-based mapping publication-title: Euphytica doi: 10.1023/A:1003950431049 – volume: 4 start-page: 250 year: 2011 end-page: 255 ident: CR12 article-title: ridge regression and other kernels for genomic selection with R package rrBLUP publication-title: The Plant Genome doi: 10.3835/plantgenome2011.08.0024 – year: 2016 ident: CR3 article-title: Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program publication-title: The Plant Genome doi: 10.3835/plantgenome2016.01.0005 – volume: 361 start-page: eaar7191 year: 2018 ident: CR25 article-title: Shifting the limits in wheat research and breeding using a fully annotated reference genome publication-title: Science doi: 10.1126/science.aar7191 – volume: 7 start-page: 327 year: 2021 end-page: 341 ident: CR48 article-title: Wheat resistance to powdery mildew is controlled by alternative splice variants encoding chimeric proteins publication-title: Nat Plants doi: 10.1038/s41477-021-00869-2 – volume: 228 start-page: 1027 issue: 3 year: 2020 end-page: 1037 ident: CR34 article-title: A CNL protein in wild emmer wheat confers powdery mildew resistance publication-title: New Phytol doi: 10.1111/nph.16761 – volume: 130 start-page: 2127 year: 2017 end-page: 2137 ident: CR11 article-title: Validation and characterization of a QTL for adult plant resistance to stripe rust on wheat chromosome arm 6BS ( ) publication-title: Theor Appl Genet doi: 10.1007/s00122-017-2946-9 – volume: 24 start-page: 211 year: 2018 end-page: 229 ident: CR67 article-title: Identification of genes from the ICE–CBF–COR pathway under cold stress in Aegilops-Triticum composite group and the evolution analysis with those from Triticeae publication-title: Physiol Mol Biol Pla doi: 10.1007/s12298-017-0495-y – volume: 119 start-page: 223 year: 2009 end-page: 230 ident: CR23 article-title: Identification and genetic mapping of pm42, a new recessive wheat powdery mildew resistance gene derived from wild emmer (Triticum turgidum var. dicoccoides) publication-title: Theor Appl Genet doi: 10.1007/s00122-009-1031-4 – volume: 131 start-page: 2085 year: 2018 end-page: 2097 ident: CR54 article-title: : a recessive gene for resistance to powdery mildew in wheat landrace Xuxusanyuehuang identified by comparative genomics analysis publication-title: Theor Appl Genet doi: 10.1007/s00122-018-3135-1 – volume: 11 start-page: 874 year: 2018 end-page: 878 ident: CR65 article-title: , Encoding a typical CC-NBS-LRR protein, encodes a CC-NBS-LRR protein conferring powdery mildew resistance in wheat publication-title: Mol Plant doi: 10.1016/j.molp.2018.02.013 – volume: 17 start-page: 37 year: 2018 end-page: 45 ident: CR66 article-title: Identification of the resistance gene to powdery mildew in Chinese wheat landrace Baiyouyantiao publication-title: J Integr Agr doi: 10.1016/S2095-3119(16)61610-6 – volume: 16 start-page: 1 issue: 3 year: 2006 end-page: 10 ident: CR15 article-title: LDheatmap: an r function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphism publication-title: J Stat Softw – volume: 101 start-page: 1753 year: 2017 end-page: 1760 ident: CR55 article-title: Effects of climate change on epidemics of powdery mildew in winter wheat in China publication-title: Plant Dis doi: 10.1094/PDIS-02-17-0168-RE – volume: 90 start-page: 980 year: 2006 end-page: 988 ident: CR68 article-title: Long-distance spread and interregional epidemics of wheat stripe rust in China publication-title: Plant Dis doi: 10.1094/PD-90-0980 – volume: 9 start-page: 166 year: 2010 end-page: 177 ident: CR26 article-title: Genomic selection in plant breeding: from theory to practice publication-title: Brief Funct Genomics doi: 10.1093/bfgp/elq001 – volume: 12 start-page: 190033 issue: 3 year: 2019 ident: CR49 article-title: Genome-wide association study of a worldwide collection of wheat genotypes reveals novel quantitative trait loci for leaf rust resistance publication-title: Plant Genome doi: 10.3835/plantgenome2019.05.0033 – volume: 11 start-page: 1353 year: 2020 ident: CR59 article-title: An ankyrin-repeat and WRKY-domain-containing immune receptor confers stripe rust resistance in wheat publication-title: Nat Commun doi: 10.1038/s41467-020-15139-6 – volume: 19 start-page: 592 year: 2014 end-page: 601 ident: CR9 article-title: Genomic selection: genome-wide prediction in plant improvement publication-title: Trends Plant Sci doi: 10.1016/j.tplants.2014.05.006 – volume: 129 start-page: 1179 year: 2016 end-page: 1189 ident: CR41 article-title: Genomic selection across multiple breeding cycles in applied bread wheat breeding publication-title: Theor Appl Genet doi: 10.1007/s00122-016-2694-2 – volume: 70 start-page: 1641 year: 2014 end-page: 1645 ident: CR43 article-title: A reassessment of the risk of rust fungi developing resistance to fungicides publication-title: Pest Manag Sci doi: 10.1002/ps.3767 – volume: 22 start-page: 26 year: 2021 ident: CR27 article-title: Homology-mediated inter-chromosomal interactions in hexaploid wheat lead to specific subgenome territories following polyploidization and introgression publication-title: Genome Biol doi: 10.1186/s13059-020-02225-7 – volume: 89 start-page: 764 year: 2017 end-page: 773 ident: CR63 article-title: Copy number variations of CBF genes at the Fr-A2 locus are essential components of winter hardiness in wheat publication-title: Plant J doi: 10.1111/tpj.13424 – volume: 8 start-page: 749 year: 2010 end-page: 771 ident: CR61 article-title: Plant responses to cold: transcriptome analysis of wheat publication-title: Plant Biotechnol J doi: 10.1111/j.1467-7652.2010.00536.x – volume: 222 start-page: 1690 year: 2019 end-page: 1704 ident: CR10 article-title: Advances and challenges in uncovering cold tolerance regulatory mechanisms in plants publication-title: New Phytol doi: 10.1111/nph.15696 – volume: 13 start-page: 1311 year: 2020 end-page: 1327 ident: CR44 article-title: High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat publication-title: Mol Plant doi: 10.1016/j.molp.2020.07.008 – volume: 588 start-page: 277 year: 2020 end-page: 283 ident: CR58 article-title: Multiple wheat genomes reveal global variation in modern breeding publication-title: Nature doi: 10.1038/s41586-020-2961-x – volume: 60 start-page: 780 year: 2018 end-page: 795 ident: CR37 article-title: Insights into the regulation of C-repeat binding factors in plant cold signaling publication-title: J Integr Plant Biol doi: 10.1111/jipb.12657 – volume: 124 start-page: 537 year: 2020 end-page: 550 ident: CR46 article-title: The progress of leaf rust research in wheat publication-title: Fungal Biol doi: 10.1016/j.funbio.2020.02.013 – year: 2016 ident: CR60 publication-title: ggplot2: Elegant Graphics for Data Analysis doi: 10.1007/978-3-319-24277-4 – volume: 15 start-page: 898 year: 2014 ident: CR69 article-title: Large-scale transcriptome comparison reveals distinct gene activations in wheat responding to stripe rust and powdery mildew publication-title: BMC Genomics doi: 10.1186/1471-2164-15-898 – year: 2017 ident: CR21 article-title: Genetic, physical and comparative mapping of the powdery mildew resistance gene Pm21 Originating from Dasypyrum villosum publication-title: Front Plant Sci doi: 10.3389/fpls.2017.01914 – volume: 10 start-page: 4023 year: 2019 ident: CR71 article-title: An ancestral NB-LRR with duplicated 3′UTRs confers stripe rust resistance in wheat and barley publication-title: Nat Commun doi: 10.1038/s41467-019-11872-9 – volume: 130 start-page: 1415 year: 2017 end-page: 1430 ident: CR28 article-title: Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat publication-title: Theor Appl Genet doi: 10.1007/s00122-017-2897-1 – volume: 55 start-page: 136 year: 2013 end-page: 147 ident: CR50 article-title: Identification of a Novel LEA Protein Involved in Freezing Tolerance in Wheat publication-title: Plant Cell Physiol doi: 10.1093/pcp/pct164 – volume: 213 start-page: 1625 year: 2017 end-page: 1631 ident: CR52 article-title: Fundamental wheat stripe rust research in the 21st century publication-title: New Phytol doi: 10.1111/nph.14159 – volume: 47 start-page: 681 year: 2017 end-page: 687 ident: CR36 article-title: Discovery and pathogenicity of CYR34, a new race of Puccinia striiformis f. sp. tritici in China publication-title: Acta Phytopathol Sin – volume: 108 start-page: 1344 year: 2018 end-page: 1354 ident: CR45 article-title: Quantitative trait loci conferring leaf rust resistance in hexaploid wheat publication-title: Phytopathology doi: 10.1094/PHYTO-06-18-0208-RVW – volume: 23 start-page: 2633 year: 2007 end-page: 2635 ident: CR5 article-title: TASSEL: software for association mapping of complex traits in diverse samples publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm308 – volume: 19 start-page: 1523 year: 2018 end-page: 1536 ident: CR13 article-title: A review of wheat diseases—a field perspective publication-title: Mol Plant Pathol doi: 10.1111/mpp.12618 – volume: 11 start-page: 879 year: 2018 end-page: 882 ident: CR22 article-title: , encoding a typical CC-NBS-LRR protein, confers broad-spectrum resistance to wheat powdery mildew disease publication-title: Mol Plant doi: 10.1016/j.molp.2018.03.004 – volume: 260–261 start-page: 154 year: 2018 end-page: 164 ident: CR64 article-title: Estimating spring frost and its impact on yield across winter wheat in China publication-title: Agr Forest Meteorol doi: 10.1016/j.agrformet.2018.06.006 – volume: 28 start-page: 2397 year: 2012 end-page: 2399 ident: CR35 article-title: GAPIT: genome association and prediction integrated tool publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts444 – year: 2020 ident: CR62 article-title: A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments publication-title: Plant Biotechnol J doi: 10.1111/pbi.13452 – volume: 129 start-page: 485 year: 2016 end-page: 493 ident: CR70 article-title: The relationship of leaf rust resistance gene and hybrid necrosis gene on wheat chromosome 2BS publication-title: Theor Appl Genet doi: 10.1007/s00122-015-2642-6 – volume: 15 start-page: 2461 year: 2016 end-page: 2468 ident: CR38 article-title: Identification of a new stripe rust resistance gene in Chinese winter wheat Zhongmai 175 publication-title: J Integr Agr doi: 10.1016/S2095-3119(16)61379-5 – volume: 69 start-page: 601 year: 2020 end-page: 617 ident: CR29 article-title: Mechanisms of powdery mildew resistance of wheat – a review of molecular breeding publication-title: Plant Pathol doi: 10.1111/ppa.13166 – volume: 47 start-page: 1494 year: 2015 end-page: 1498 ident: CR42 article-title: A recently evolved hexose transporter variant confers resistance to multiple pathogens in wheat publication-title: Nat Genet doi: 10.1038/ng.3439 – volume: 104 start-page: 743 year: 2020 end-page: 751 ident: CR33 article-title: Identification of a recessive gene conferring resistance to powdery mildew in wheat landrace Qingxinmai using BSR-Seq analysis publication-title: Plant Dis doi: 10.1094/PDIS-08-19-1745-RE – volume: 97 start-page: 56 year: 2019 end-page: 72 ident: CR4 article-title: Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat publication-title: Plant J – volume: 323 start-page: 1357 year: 2009 end-page: 1360 ident: CR14 article-title: A kinase-START gene confers temperature-dependent resistance to wheat stripe rust publication-title: Science doi: 10.1126/science.1166289 – volume: 65 start-page: 93 year: 2007 end-page: 106 ident: CR7 article-title: Leaf rust resistance gene , isolated from bread wheat ( L.) is a member of the large psr567 gene family publication-title: Plant Mol Biol doi: 10.1007/s11103-007-9201-8 – volume: 242 start-page: 23 year: 2016 end-page: 36 ident: CR2 article-title: Breeding schemes for the implementation of genomic selection in wheat ( spp.) publication-title: Plant Sci doi: 10.1016/j.plantsci.2015.08.021 – volume: 1 start-page: 49 year: 1988 ident: CR53 article-title: Scoring powdery mildew with infection type at wheat seedling stage publication-title: Plant Prot – volume: 127 start-page: 1795 year: 2014 end-page: 1803 ident: CR8 article-title: Genomic prediction for rust resistance in diverse wheat landraces publication-title: Theor Appl Genet doi: 10.1007/s00122-014-2341-8 – volume: 11 start-page: 680 year: 2020 ident: CR39 article-title: A rare gain of function mutation in a wheat tandem kinase confers resistance to powdery mildew publication-title: Nat Commun doi: 10.1038/s41467-020-14294-0 – ident: CR20 – volume: 128 start-page: 465 year: 2015 end-page: 476 ident: CR18 article-title: Molecular characterization of a new powdery mildew resistance gene Pm54 in soft red winter wheat publication-title: Theor Appl Genet doi: 10.1007/s00122-014-2445-1 – volume: 323 start-page: 1360 year: 2009 ident: 3863_CR31 publication-title: Science doi: 10.1126/science.1166453 – volume: 97 start-page: 56 year: 2019 ident: 3863_CR4 publication-title: Plant J doi: 10.1111/tpj.14150 – volume: 4 start-page: 250 year: 2011 ident: 3863_CR12 publication-title: The Plant Genome doi: 10.3835/plantgenome2011.08.0024 – year: 2016 ident: 3863_CR3 publication-title: The Plant Genome doi: 10.3835/plantgenome2016.01.0005 – volume: 65 start-page: 93 year: 2007 ident: 3863_CR7 publication-title: Plant Mol Biol doi: 10.1007/s11103-007-9201-8 – volume: 12 start-page: 190033 issue: 3 year: 2019 ident: 3863_CR49 publication-title: Plant Genome doi: 10.3835/plantgenome2019.05.0033 – volume: 101 start-page: 1753 year: 2017 ident: 3863_CR55 publication-title: Plant Dis doi: 10.1094/PDIS-02-17-0168-RE – volume: 13 start-page: 1311 year: 2020 ident: 3863_CR44 publication-title: Mol Plant doi: 10.1016/j.molp.2020.07.008 – volume: 19 start-page: 1523 year: 2018 ident: 3863_CR13 publication-title: Mol Plant Pathol doi: 10.1111/mpp.12618 – volume: 108 start-page: 1344 year: 2018 ident: 3863_CR45 publication-title: Phytopathology doi: 10.1094/PHYTO-06-18-0208-RVW – volume: 22 start-page: 26 year: 2021 ident: 3863_CR27 publication-title: Genome Biol doi: 10.1186/s13059-020-02225-7 – volume: 7 start-page: 327 year: 2021 ident: 3863_CR48 publication-title: Nat Plants doi: 10.1038/s41477-021-00869-2 – volume: 70 start-page: 1641 year: 2014 ident: 3863_CR43 publication-title: Pest Manag Sci doi: 10.1002/ps.3767 – volume: 127 start-page: 2267 year: 2014 ident: 3863_CR6 publication-title: Theor Appl Genet doi: 10.1007/s00122-014-2378-8 – volume: 17 start-page: 37 year: 2018 ident: 3863_CR66 publication-title: J Integr Agr doi: 10.1016/S2095-3119(16)61610-6 – start-page: 293 volume-title: Crop production and global environmental issues year: 2015 ident: 3863_CR1 doi: 10.1007/978-3-319-23162-4_13 – volume: 8 start-page: 749 year: 2010 ident: 3863_CR61 publication-title: Plant Biotechnol J doi: 10.1111/j.1467-7652.2010.00536.x – volume: 47 start-page: 1494 year: 2015 ident: 3863_CR42 publication-title: Nat Genet doi: 10.1038/ng.3439 – year: 2017 ident: 3863_CR21 publication-title: Front Plant Sci doi: 10.3389/fpls.2017.01914 – volume-title: ggplot2: Elegant Graphics for Data Analysis year: 2016 ident: 3863_CR60 doi: 10.1007/978-3-319-24277-4 – volume: 15 start-page: 2461 year: 2016 ident: 3863_CR38 publication-title: J Integr Agr doi: 10.1016/S2095-3119(16)61379-5 – volume: 60 start-page: 780 year: 2018 ident: 3863_CR37 publication-title: J Integr Plant Biol doi: 10.1111/jipb.12657 – volume: 11 start-page: 5085 year: 2020 ident: 3863_CR17 publication-title: Nat Commun doi: 10.1038/s41467-020-18738-5 – volume: 5 start-page: S12 year: 2011 ident: 3863_CR51 publication-title: BMC Proceed doi: 10.1186/1753-6561-5-S3-S12 – volume: 11 start-page: 1353 year: 2020 ident: 3863_CR59 publication-title: Nat Commun doi: 10.1038/s41467-020-15139-6 – volume: 115 start-page: 121 year: 2000 ident: 3863_CR47 publication-title: Euphytica doi: 10.1023/A:1003950431049 – volume: 7 start-page: 775 issue: 3 year: 2017 ident: 3863_CR32 publication-title: G3-Genes Genom Genet doi: 10.1534/g3.116.037622 – volume: 9 start-page: 166 year: 2010 ident: 3863_CR26 publication-title: Brief Funct Genomics doi: 10.1093/bfgp/elq001 – volume: 47 start-page: 681 year: 2017 ident: 3863_CR36 publication-title: Acta Phytopathol Sin – ident: 3863_CR20 – volume: 28 start-page: 2397 year: 2012 ident: 3863_CR35 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts444 – year: 2020 ident: 3863_CR62 publication-title: Plant Biotechnol J doi: 10.1111/pbi.13452 – volume: 129 start-page: 1179 year: 2016 ident: 3863_CR41 publication-title: Theor Appl Genet doi: 10.1007/s00122-016-2694-2 – volume: 24 start-page: 211 year: 2018 ident: 3863_CR67 publication-title: Physiol Mol Biol Pla doi: 10.1007/s12298-017-0495-y – volume: 157 start-page: 1819 year: 2001 ident: 3863_CR40 publication-title: Genetics doi: 10.1093/genetics/157.4.1819 – volume: 260–261 start-page: 154 year: 2018 ident: 3863_CR64 publication-title: Agr Forest Meteorol doi: 10.1016/j.agrformet.2018.06.006 – volume: 127 start-page: 1795 year: 2014 ident: 3863_CR8 publication-title: Theor Appl Genet doi: 10.1007/s00122-014-2341-8 – volume: 128 start-page: 465 year: 2015 ident: 3863_CR18 publication-title: Theor Appl Genet doi: 10.1007/s00122-014-2445-1 – volume: 104 start-page: 743 year: 2020 ident: 3863_CR33 publication-title: Plant Dis doi: 10.1094/PDIS-08-19-1745-RE – volume: 11 start-page: 879 year: 2018 ident: 3863_CR22 publication-title: Mol Plant doi: 10.1016/j.molp.2018.03.004 – volume: 222 start-page: 1690 year: 2019 ident: 3863_CR10 publication-title: New Phytol doi: 10.1111/nph.15696 – volume: 124 start-page: 537 year: 2020 ident: 3863_CR46 publication-title: Fungal Biol doi: 10.1016/j.funbio.2020.02.013 – volume: 19 start-page: 592 year: 2014 ident: 3863_CR9 publication-title: Trends Plant Sci doi: 10.1016/j.tplants.2014.05.006 – volume: 242 start-page: 23 year: 2016 ident: 3863_CR2 publication-title: Plant Sci doi: 10.1016/j.plantsci.2015.08.021 – volume: 23 start-page: 2633 year: 2007 ident: 3863_CR5 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm308 – volume: 62 start-page: 258 year: 2020 ident: 3863_CR56 publication-title: J Integr Plant Biol doi: 10.1111/jipb.12918 – volume: 11 start-page: 874 year: 2018 ident: 3863_CR65 publication-title: Mol Plant doi: 10.1016/j.molp.2018.02.013 – volume: 6 start-page: 1 year: 2017 ident: 3863_CR72 publication-title: Gigascience doi: 10.1093/gigascience/gix097 – volume: 16 start-page: 1 issue: 3 year: 2006 ident: 3863_CR15 publication-title: J Stat Softw – volume: 11 start-page: e0150717 year: 2016 ident: 3863_CR19 publication-title: PLoS ONE doi: 10.1371/journal.pone.0150717 – volume: 69 start-page: 601 year: 2020 ident: 3863_CR29 publication-title: Plant Pathol doi: 10.1111/ppa.13166 – volume: 323 start-page: 1357 year: 2009 ident: 3863_CR14 publication-title: Science doi: 10.1126/science.1166289 – volume: 213 start-page: 1625 year: 2017 ident: 3863_CR52 publication-title: New Phytol doi: 10.1111/nph.14159 – volume: 361 start-page: eaar7191 year: 2018 ident: 3863_CR25 publication-title: Science doi: 10.1126/science.aar7191 – volume: 1 start-page: 49 year: 1988 ident: 3863_CR53 publication-title: Plant Prot – volume: 130 start-page: 1415 year: 2017 ident: 3863_CR28 publication-title: Theor Appl Genet doi: 10.1007/s00122-017-2897-1 – volume: 130 start-page: 2127 year: 2017 ident: 3863_CR11 publication-title: Theor Appl Genet doi: 10.1007/s00122-017-2946-9 – volume: 131 start-page: 2085 year: 2018 ident: 3863_CR54 publication-title: Theor Appl Genet doi: 10.1007/s00122-018-3135-1 – volume: 10 start-page: 4023 year: 2019 ident: 3863_CR71 publication-title: Nat Commun doi: 10.1038/s41467-019-11872-9 – volume: 90 start-page: 980 year: 2006 ident: 3863_CR68 publication-title: Plant Dis doi: 10.1094/PD-90-0980 – volume: 11 start-page: 680 year: 2020 ident: 3863_CR39 publication-title: Nat Commun doi: 10.1038/s41467-020-14294-0 – volume: 35 start-page: 793 year: 2017 ident: 3863_CR57 publication-title: Nat Biotechnol doi: 10.1038/nbt.3877 – volume: 129 start-page: 485 year: 2016 ident: 3863_CR70 publication-title: Theor Appl Genet doi: 10.1007/s00122-015-2642-6 – volume: 179 start-page: 143 year: 2011 ident: 3863_CR24 publication-title: Euphytica doi: 10.1007/s10681-011-0361-x – volume: 588 start-page: 277 year: 2020 ident: 3863_CR58 publication-title: Nature doi: 10.1038/s41586-020-2961-x – volume: 89 start-page: 764 year: 2017 ident: 3863_CR63 publication-title: Plant J doi: 10.1111/tpj.13424 – volume: 108 start-page: 246 year: 2018 ident: 3863_CR30 publication-title: Phytopathology doi: 10.1094/PHYTO-07-17-0254-R – volume: 15 start-page: 898 year: 2014 ident: 3863_CR69 publication-title: BMC Genomics doi: 10.1186/1471-2164-15-898 – volume: 7 start-page: e8190 year: 2019 ident: 3863_CR16 publication-title: PeerJ doi: 10.7717/peerj.8190 – volume: 119 start-page: 223 year: 2009 ident: 3863_CR23 publication-title: Theor Appl Genet doi: 10.1007/s00122-009-1031-4 – volume: 228 start-page: 1027 issue: 3 year: 2020 ident: 3863_CR34 publication-title: New Phytol doi: 10.1111/nph.16761 – volume: 55 start-page: 136 year: 2013 ident: 3863_CR50 publication-title: Plant Cell Physiol doi: 10.1093/pcp/pct164 |
| SSID | ssj0002503 |
| Score | 2.478233 |
| Snippet | Key message
High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic... Key message Key message High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic... Key messageHigh-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic... High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction... KEY MESSAGE: High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2857 |
| SubjectTerms | Agricultural research Agriculture Biochemistry Biomedical and Life Sciences Biotechnology Calories Chromosome mapping Cold tolerance Control Cultivars Disease resistance Diseases and pests Fungal diseases of plants Gene mapping genes Genetic aspects Genome-wide association studies genome-wide association study Genomes Genomics Genotyping genotyping by sequencing Hardiness Leaf rust Life Sciences marker-assisted selection Methods Original Article Plant Biochemistry Plant breeding Plant Breeding/Biotechnology Plant Genetics and Genomics Plant immunology Plants Powdery mildew prediction Prediction models Quantitative trait loci quantitative traits Single-nucleotide polymorphism staple crops stress tolerance Stripe rust Wheat |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection (ProQuest) dbid: 7X7 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwEB7BAhIceCwgAgsyCIkDWNvGiZOcUIVYwWWFeEi9WX5lVWlJStJS8e-ZcdxUBdELt6ievDrjmc-Z8TcAL3XhC3S9kirVak6EchwPapx4vpaTzGLQKEOzieL8vJzPq0_xg1sfyyq3PjE4atda-kZ-OlBjCZFlb5c_OHWNouxqbKFxFa5R22yy82I-LrgovI9VcwhL0rhpJmydCzklTgUKE1FKweVeYPrTPf-VJw3h5-zO_z74XbgdgSebDZZyD6745hhuzS66SL7hj-HG0Jjy133YUPkHx5V4NExGVK7fPd8snGd6p1EW2GmZbtwgsbBs2VHqJwwiHmYxAcTwWoRU0cSCNJqfY6v20nfhp0XDNhQVHsC3s_df333gsUUDt1kpV7wkPvu6klpal2lRWSdzMfG2mphcI1TRwtU5juQmt4WYaimN1jWt6lAqLYx4CEdN2_hHwLwxtUa85_LSI0YzpirNVNtUONoGmFUJTLf6UTbyl1MbjUs1Mi8HnSrUqQo6VTKB1-M5y4G946D0C1K7IlqMhupuLvS679XHL5_VTBYYGhALpQm8ikJ1i7e3Om5jwJcgJq09yZM9SZy3dn94ay8q-o1e7YwlgefjMJ1JtXCNb9ckI2QmiartgIwUCC0rWRUJvNla7u42__4PHh9-qidwMw1zhorsTuBo1a39U7huf64WffcszL7fBIEz2g priority: 102 providerName: ProQuest |
| Title | High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat |
| URI | https://link.springer.com/article/10.1007/s00122-021-03863-6 https://www.proquest.com/docview/2559953344 https://www.proquest.com/docview/2536464305 https://www.proquest.com/docview/2636589697 |
| Volume | 134 |
| WOSCitedRecordID | wos000656785900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: Springer LINK customDbUrl: eissn: 1432-2242 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002503 issn: 0040-5752 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED-xDSR44GMMERiVQUg8gKU2TuzksaBN8FJVG6C-WY7jTJVGOiUtFf89d46TqXxMgpcoin_5ss93Z93dzwCvjXIKVa-kTLWKE6Ecx5MKJ56r5DixaDQyv9mEms2yxSKfh6Kwts9270OSXlMPxW4-CsQppWAsMim43IODlNhmaI1-_nXQv2jUh1w5dEbiUCrz52fsmKNflfJv0VFvdE4f_N_nPoT7wclk004qHsEtVx_CvelFE4g23CHc6Tah_PEYtpTqwXHVHYSQEW3rN8e3y9Ixcz16zDPRMlOXHWJp2VVDYR7fiL4vC8Eehs8irxTFyaNR1Eq2Xl26xl9a1mxLFuAIvpyefP7wkYftGLhNMrnmGXHXV7k00paJEbktZSrGzubjIjXolhhRVim2pEVqlZgYKQtjKlrBISpWhXgC-_Wqdk-BuaKoDPp2ZZo59MeKIs-KibGxKKnkL8kjmPSjom3gKqctMy71wLLsu1dj92rfvVpG8Ha456pj6rgR_YoGWxMFRk05Nhdm07b60_mZnkqFZgD9njiCNwFUrfD11oSSBfwJYs3aQR7vIHGO2t3mXqZ00BGt7sjehEiSCF4OzXQn5b3VbrUhjJCJJFq2GzBSoBuZy1xF8K6XxevX_L0Pnv0b_Dncjb04U4LdMeyvm417Abft9_WybUawpxbKH7MRHLw_mc3PRpRIOx_52fkTeUYuFQ |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VAgIOPEoRhgILAnGgFo7XXtsHhCKgatQSVdBKvS3r9bqKVOxgJ0T9U_xGZtaPKCBy64FblB3b8fqbhzMz3wC8VJGJ0PQKqlTLXSKUc_FDjopncuEFGp1GbIdNRONxfHqaHG3Ar64XhsoqO5toDXVWavqP_G1DjcV5ELyf_nBpahRlV7sRGg0sDszFAl_Z6nejj_h8X_n-3qfjD_tuO1XA1UEsZm5MFOx5IpTQWaB4ojMRcs_oxEtDhd5V8SwPcSVMQx3xgRIiVSqnFxGU8qOU43mvwFUMI3zPlgoe9ZYfw4m-Sg_DIL9t0rGtejaH5VJBhMdjwV2x4gj_dAd_5WWtu9u7879t1F243QbWbNhowj3YMMUW3BqeVS25iNmC683gzYv7sKDyFrcyneIxoqr9btzFJDNMLRHLLPsuU0XWSEw0m1aU2rKLGO-zNsHF8FwUiaMKWWlUr4zNynNT2a8mBVuQ19uGk0vZgwewWZSFeQjMpGmuMJ7NwtggeNI0idOB0j7PqM0xSBwYdHiQuuVnpzEh57JnlrYYkoghaTEkhQNv-mOmDTvJWukXBDNJtB8F1RWdqXldy9HXL3IoInR9GOv5DrxuhfISL69V26aBN0FMYSuSOyuSaJf06nKHT9naxVouwenA836ZjqRav8KUc5LhIhBERbdGRnAMnRORRA7sdpqyvMy_9-DR-l_1DG7sH38-lIej8cFjuOlbfaWCwh3YnFVz8wSu6Z-zSV09tZrP4Ntla9BvMuSQxQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFBAceJQiAgUWBOJArSZee20fEAq0EVFRFBUq9bas1-sqUrGDkxD1r_HrmFmvEwVEbj1wi7JjO9l889jMzDcAr1RkIjS9girVco8I5Tx8kaPimVx0Ao1OI7bDJqLhMD47S0Zb8KvphaGyysYmWkOdlZr-Iz-oqbE4D4KD3JVFjA777yc_PJogRZnWZpxGDZFjc7nA49v03eAQf-vXvt8_-vrxk-cmDHg6iMXMi4mOPU-EEjoLFE90JkLeMTrppKFCT6t4loe4EqahjnhXCZEqldOhBKX8KOV432uwHXE89LRg-8PRcHSy9AMYXCxr9jAo8l3Ljm3csxktj8ojOjwW3BNrbvFP5_BXltY6v_7d_3nb7sEdF3KzXq0j92HLFDtwu3deOdoRswM36pGclw9gQYUvXmUalWREYvvdeItxZphaYZlZXl6miqyWGGs2qSjpZRfxJMBc6ovhvShGR-Wy0qh4GZuVF6ayb40LtiB_uAunV7IHD6FVlIV5BMykaa4w0s3C2GB0mqZJnHaV9nlGDZBB0oZugw2pHXM7DRC5kEvOaYsniXiSFk9StOHt8ppJzVuyUfolQU4SIUhBGDlX8-lUDr6cyJ6I0CliFOi34Y0Tykt8vFaugQO_BHGIrUnurUmixdLryw1WpbOYU7kCahteLJfpSqoCLEw5JxkuAkEkdRtkBMegOhFJ1Ib9RmtWj_n3Hjze_Kmew01UHPl5MDx-Ard8q7pUabgHrVk1N0_huv45G0-rZ84MMPh21Sr0G-UImt0 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=High-resolution+genome-wide+association+study+and+genomic+prediction+for+disease+resistance+and+cold+tolerance+in+wheat&rft.jtitle=Theoretical+and+applied+genetics&rft.au=Pang%2C+Yunlong&rft.au=Wu%2C+Yuye&rft.au=Liu%2C+Chunxia&rft.au=Li%2C+Wenhui&rft.date=2021-09-01&rft.pub=Springer&rft.issn=0040-5752&rft.volume=134&rft.issue=9&rft.spage=2857&rft_id=info:doi/10.1007%2Fs00122-021-03863-6&rft.externalDBID=ISR&rft.externalDocID=A671531242 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0040-5752&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0040-5752&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0040-5752&client=summon |