Whole-genome mining of abiotic stress gene loci in rice
Main conclusion We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromos...
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| creator | Yang, Luomiao Lei, Lei Liu, HuaLong Wang, Jingguo Zheng, Hongliang Zou, Detang |
| description | Main conclusion
We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromosome intervals for mining abiotic stress candidate genes were obtained.
Drought, severe cold, high salinity, and high metallic ion concentrations severely restrict rice production. Consequently, the breeding of abiotic stress-tolerant variety is being paid increasingly more attention. This study aimed to identify meta-quantitative trait loci (MQTL) for abiotic stress tolerance in rice, as well as the molecular markers and potential candidate genes of the MQTL regions. We summarized 2785 rice QTL and conducted a meta-analysis of 159 studies. We found 82 drought tolerance (DT), 70 cold tolerance (CT), 70 salt tolerance (ST), and 51 heavy metal ion tolerance (IT) meta-QTL, as well as 20 DT, 11 CT, 22 ST, and 5 IT candidate genes in the MQTL interval. Thirty-one multiple-tolerance related MQTL regions, which were highly enriched, were also detected, and 13 candidate genes related to multiple-tolerance were obtained. In addition, the correlation between DT, CT, and ST was significant in the rice genome. Four candidate genes and four MM-QTL regions were detected simultaneously by GWAS and meta-analysis. The four candidate genes showed distinct genetic differentiation and substantial genetic distance between
indica
and
japonica
rice, and the four MM-QTL are potential intervals for mining abiotic stress-related candidate genes. The candidate genes identified in this study will not only be useful for marker-assisted selection and pyramiding but will also accelerate the fine mapping and cloning of the candidate genes associated with abiotic stress-tolerance mechanisms in rice. |
| doi_str_mv | 10.1007/s00425-020-03488-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2451135748</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2471925296</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-652e25b4fd46be65e52bc58280f69b27cb0a1ff0c168fc7c2a1aaaabff0f2eee3</originalsourceid><addsrcrecordid>eNp9kE1LAzEURYMotlb_gAsZcONm9OVrMrOU4hcU3Cguw0z6UlNmJjXpQP33RlsVXJhNQu55N-EQckrhkgKoqwggmMyBQQ5clGW-2SNjKjjLGYhyn4wB0hkqLkfkKMYlQAqVOiQjzkEyofiYqJdX32K-wN53mHWud_0i8zarG-fXzmRxHTDGLOWYtd64zPVZcAaPyYGt24gnu31Cnm9vnqb3-ezx7mF6PcsNV3KdF5Ihk42wc1E0WEiUrDGyZCXYomqYMg3U1FowtCitUYbVtE6rSVeWISKfkItt7yr4twHjWncuGmzbukc_RM2EpJRLJcqEnv9Bl34IffpdohStmGRVkSi2pUzwMQa0ehVcV4d3TUF_atVbrTpp1V9a9SYNne2qh6bD-c_It8cE8C0QU9QvMPy-_U_tB0xlgmc</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2471925296</pqid></control><display><type>article</type><title>Whole-genome mining of abiotic stress gene loci in rice</title><source>Jstor Complete Legacy</source><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Yang, Luomiao ; Lei, Lei ; Liu, HuaLong ; Wang, Jingguo ; Zheng, Hongliang ; Zou, Detang</creator><creatorcontrib>Yang, Luomiao ; Lei, Lei ; Liu, HuaLong ; Wang, Jingguo ; Zheng, Hongliang ; Zou, Detang</creatorcontrib><description>Main conclusion
We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromosome intervals for mining abiotic stress candidate genes were obtained.
Drought, severe cold, high salinity, and high metallic ion concentrations severely restrict rice production. Consequently, the breeding of abiotic stress-tolerant variety is being paid increasingly more attention. This study aimed to identify meta-quantitative trait loci (MQTL) for abiotic stress tolerance in rice, as well as the molecular markers and potential candidate genes of the MQTL regions. We summarized 2785 rice QTL and conducted a meta-analysis of 159 studies. We found 82 drought tolerance (DT), 70 cold tolerance (CT), 70 salt tolerance (ST), and 51 heavy metal ion tolerance (IT) meta-QTL, as well as 20 DT, 11 CT, 22 ST, and 5 IT candidate genes in the MQTL interval. Thirty-one multiple-tolerance related MQTL regions, which were highly enriched, were also detected, and 13 candidate genes related to multiple-tolerance were obtained. In addition, the correlation between DT, CT, and ST was significant in the rice genome. Four candidate genes and four MM-QTL regions were detected simultaneously by GWAS and meta-analysis. The four candidate genes showed distinct genetic differentiation and substantial genetic distance between
indica
and
japonica
rice, and the four MM-QTL are potential intervals for mining abiotic stress-related candidate genes. The candidate genes identified in this study will not only be useful for marker-assisted selection and pyramiding but will also accelerate the fine mapping and cloning of the candidate genes associated with abiotic stress-tolerance mechanisms in rice.</description><identifier>ISSN: 0032-0935</identifier><identifier>EISSN: 1432-2048</identifier><identifier>DOI: 10.1007/s00425-020-03488-x</identifier><identifier>PMID: 33052473</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abiotic stress ; Agriculture ; Association analysis ; Biomarkers ; Biomedical and Life Sciences ; Chromosomes ; Cloning ; Cold tolerance ; Crop production ; Data Mining ; Drought resistance ; Ecology ; Forestry ; Gene loci ; Gene mapping ; Genes ; Genetic distance ; Genome-Wide Association Study ; Genomes ; Heavy metals ; Intervals ; Life Sciences ; Marker-assisted selection ; Meta-analysis ; Metal concentrations ; Metal ions ; Original Article ; Oryza - genetics ; Plant breeding ; Plant Sciences ; Quantitative trait loci ; Quantitative Trait Loci - genetics ; Rice ; Salinity ; Salinity effects ; Salinity tolerance ; Salt tolerance ; Stress, Physiological - genetics</subject><ispartof>Planta, 2020-11, Vol.252 (5), p.85-85, Article 85</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-652e25b4fd46be65e52bc58280f69b27cb0a1ff0c168fc7c2a1aaaabff0f2eee3</citedby><cites>FETCH-LOGICAL-c375t-652e25b4fd46be65e52bc58280f69b27cb0a1ff0c168fc7c2a1aaaabff0f2eee3</cites><orcidid>0000-0003-0835-0753</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00425-020-03488-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00425-020-03488-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33052473$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Luomiao</creatorcontrib><creatorcontrib>Lei, Lei</creatorcontrib><creatorcontrib>Liu, HuaLong</creatorcontrib><creatorcontrib>Wang, Jingguo</creatorcontrib><creatorcontrib>Zheng, Hongliang</creatorcontrib><creatorcontrib>Zou, Detang</creatorcontrib><title>Whole-genome mining of abiotic stress gene loci in rice</title><title>Planta</title><addtitle>Planta</addtitle><addtitle>Planta</addtitle><description>Main conclusion
We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromosome intervals for mining abiotic stress candidate genes were obtained.
Drought, severe cold, high salinity, and high metallic ion concentrations severely restrict rice production. Consequently, the breeding of abiotic stress-tolerant variety is being paid increasingly more attention. This study aimed to identify meta-quantitative trait loci (MQTL) for abiotic stress tolerance in rice, as well as the molecular markers and potential candidate genes of the MQTL regions. We summarized 2785 rice QTL and conducted a meta-analysis of 159 studies. We found 82 drought tolerance (DT), 70 cold tolerance (CT), 70 salt tolerance (ST), and 51 heavy metal ion tolerance (IT) meta-QTL, as well as 20 DT, 11 CT, 22 ST, and 5 IT candidate genes in the MQTL interval. Thirty-one multiple-tolerance related MQTL regions, which were highly enriched, were also detected, and 13 candidate genes related to multiple-tolerance were obtained. In addition, the correlation between DT, CT, and ST was significant in the rice genome. Four candidate genes and four MM-QTL regions were detected simultaneously by GWAS and meta-analysis. The four candidate genes showed distinct genetic differentiation and substantial genetic distance between
indica
and
japonica
rice, and the four MM-QTL are potential intervals for mining abiotic stress-related candidate genes. The candidate genes identified in this study will not only be useful for marker-assisted selection and pyramiding but will also accelerate the fine mapping and cloning of the candidate genes associated with abiotic stress-tolerance mechanisms in rice.</description><subject>Abiotic stress</subject><subject>Agriculture</subject><subject>Association analysis</subject><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Chromosomes</subject><subject>Cloning</subject><subject>Cold tolerance</subject><subject>Crop production</subject><subject>Data Mining</subject><subject>Drought resistance</subject><subject>Ecology</subject><subject>Forestry</subject><subject>Gene loci</subject><subject>Gene mapping</subject><subject>Genes</subject><subject>Genetic distance</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Heavy metals</subject><subject>Intervals</subject><subject>Life Sciences</subject><subject>Marker-assisted selection</subject><subject>Meta-analysis</subject><subject>Metal concentrations</subject><subject>Metal ions</subject><subject>Original Article</subject><subject>Oryza - genetics</subject><subject>Plant breeding</subject><subject>Plant Sciences</subject><subject>Quantitative trait loci</subject><subject>Quantitative Trait Loci - genetics</subject><subject>Rice</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Salinity tolerance</subject><subject>Salt tolerance</subject><subject>Stress, Physiological - genetics</subject><issn>0032-0935</issn><issn>1432-2048</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kE1LAzEURYMotlb_gAsZcONm9OVrMrOU4hcU3Cguw0z6UlNmJjXpQP33RlsVXJhNQu55N-EQckrhkgKoqwggmMyBQQ5clGW-2SNjKjjLGYhyn4wB0hkqLkfkKMYlQAqVOiQjzkEyofiYqJdX32K-wN53mHWud_0i8zarG-fXzmRxHTDGLOWYtd64zPVZcAaPyYGt24gnu31Cnm9vnqb3-ezx7mF6PcsNV3KdF5Ihk42wc1E0WEiUrDGyZCXYomqYMg3U1FowtCitUYbVtE6rSVeWISKfkItt7yr4twHjWncuGmzbukc_RM2EpJRLJcqEnv9Bl34IffpdohStmGRVkSi2pUzwMQa0ehVcV4d3TUF_atVbrTpp1V9a9SYNne2qh6bD-c_It8cE8C0QU9QvMPy-_U_tB0xlgmc</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Yang, Luomiao</creator><creator>Lei, Lei</creator><creator>Liu, HuaLong</creator><creator>Wang, Jingguo</creator><creator>Zheng, Hongliang</creator><creator>Zou, Detang</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7QR</scope><scope>7TM</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0835-0753</orcidid></search><sort><creationdate>20201101</creationdate><title>Whole-genome mining of abiotic stress gene loci in rice</title><author>Yang, Luomiao ; Lei, Lei ; Liu, HuaLong ; Wang, Jingguo ; Zheng, Hongliang ; Zou, Detang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-652e25b4fd46be65e52bc58280f69b27cb0a1ff0c168fc7c2a1aaaabff0f2eee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Abiotic stress</topic><topic>Agriculture</topic><topic>Association analysis</topic><topic>Biomarkers</topic><topic>Biomedical and Life Sciences</topic><topic>Chromosomes</topic><topic>Cloning</topic><topic>Cold tolerance</topic><topic>Crop production</topic><topic>Data Mining</topic><topic>Drought resistance</topic><topic>Ecology</topic><topic>Forestry</topic><topic>Gene loci</topic><topic>Gene mapping</topic><topic>Genes</topic><topic>Genetic distance</topic><topic>Genome-Wide Association Study</topic><topic>Genomes</topic><topic>Heavy metals</topic><topic>Intervals</topic><topic>Life Sciences</topic><topic>Marker-assisted selection</topic><topic>Meta-analysis</topic><topic>Metal concentrations</topic><topic>Metal ions</topic><topic>Original Article</topic><topic>Oryza - genetics</topic><topic>Plant breeding</topic><topic>Plant Sciences</topic><topic>Quantitative trait loci</topic><topic>Quantitative Trait Loci - genetics</topic><topic>Rice</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Salinity tolerance</topic><topic>Salt tolerance</topic><topic>Stress, Physiological - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Luomiao</creatorcontrib><creatorcontrib>Lei, Lei</creatorcontrib><creatorcontrib>Liu, HuaLong</creatorcontrib><creatorcontrib>Wang, Jingguo</creatorcontrib><creatorcontrib>Zheng, Hongliang</creatorcontrib><creatorcontrib>Zou, Detang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Planta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Luomiao</au><au>Lei, Lei</au><au>Liu, HuaLong</au><au>Wang, Jingguo</au><au>Zheng, Hongliang</au><au>Zou, Detang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Whole-genome mining of abiotic stress gene loci in rice</atitle><jtitle>Planta</jtitle><stitle>Planta</stitle><addtitle>Planta</addtitle><date>2020-11-01</date><risdate>2020</risdate><volume>252</volume><issue>5</issue><spage>85</spage><epage>85</epage><pages>85-85</pages><artnum>85</artnum><issn>0032-0935</issn><eissn>1432-2048</eissn><abstract>Main conclusion
We projected meta-QTL (MQTL) for drought, salinity, cold state, and high metal ion tolerance in rice using a meta-analysis based on high-density consensus maps. In addition, a genome-wide association analysis was used to validate the results of the meta-analysis, and four new chromosome intervals for mining abiotic stress candidate genes were obtained.
Drought, severe cold, high salinity, and high metallic ion concentrations severely restrict rice production. Consequently, the breeding of abiotic stress-tolerant variety is being paid increasingly more attention. This study aimed to identify meta-quantitative trait loci (MQTL) for abiotic stress tolerance in rice, as well as the molecular markers and potential candidate genes of the MQTL regions. We summarized 2785 rice QTL and conducted a meta-analysis of 159 studies. We found 82 drought tolerance (DT), 70 cold tolerance (CT), 70 salt tolerance (ST), and 51 heavy metal ion tolerance (IT) meta-QTL, as well as 20 DT, 11 CT, 22 ST, and 5 IT candidate genes in the MQTL interval. Thirty-one multiple-tolerance related MQTL regions, which were highly enriched, were also detected, and 13 candidate genes related to multiple-tolerance were obtained. In addition, the correlation between DT, CT, and ST was significant in the rice genome. Four candidate genes and four MM-QTL regions were detected simultaneously by GWAS and meta-analysis. The four candidate genes showed distinct genetic differentiation and substantial genetic distance between
indica
and
japonica
rice, and the four MM-QTL are potential intervals for mining abiotic stress-related candidate genes. The candidate genes identified in this study will not only be useful for marker-assisted selection and pyramiding but will also accelerate the fine mapping and cloning of the candidate genes associated with abiotic stress-tolerance mechanisms in rice.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33052473</pmid><doi>10.1007/s00425-020-03488-x</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-0835-0753</orcidid></addata></record> |
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| subjects | Abiotic stress Agriculture Association analysis Biomarkers Biomedical and Life Sciences Chromosomes Cloning Cold tolerance Crop production Data Mining Drought resistance Ecology Forestry Gene loci Gene mapping Genes Genetic distance Genome-Wide Association Study Genomes Heavy metals Intervals Life Sciences Marker-assisted selection Meta-analysis Metal concentrations Metal ions Original Article Oryza - genetics Plant breeding Plant Sciences Quantitative trait loci Quantitative Trait Loci - genetics Rice Salinity Salinity effects Salinity tolerance Salt tolerance Stress, Physiological - genetics |
| title | Whole-genome mining of abiotic stress gene loci in rice |
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