Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure
Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and Zheng-Yang × M8206 was tested for DT using polyethylene-glycol (PEG) treatme...
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description | Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and Zheng-Yang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) = 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL–environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52–41.65 %), plus genetic variation (46.90–58.40%) in a total of 88.55–95.92% PV. The identified QTLs–alleles were organized into main-effect and QEI-effect QTL–allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098–1.132, transgressive over the parents (0.651–0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL–allele-based genomic selection for optimal cross selection. |
doi_str_mv | 10.1007/s00425-018-2952-4 |
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Merr.] using a novel GWAS procedure</title><source>Jstor Complete Legacy</source><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Khan, Mueen Alam ; Tong, Fei ; Wang, Wubin ; He, Jianbo ; Zhao, Tuanjie ; Gai, Junyi</creator><creatorcontrib>Khan, Mueen Alam ; Tong, Fei ; Wang, Wubin ; He, Jianbo ; Zhao, Tuanjie ; Gai, Junyi</creatorcontrib><description>Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and Zheng-Yang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) = 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL–environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52–41.65 %), plus genetic variation (46.90–58.40%) in a total of 88.55–95.92% PV. The identified QTLs–alleles were organized into main-effect and QEI-effect QTL–allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098–1.132, transgressive over the parents (0.651–0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL–allele-based genomic selection for optimal cross selection.</description><identifier>ISSN: 0032-0935</identifier><identifier>EISSN: 1432-2048</identifier><identifier>DOI: 10.1007/s00425-018-2952-4</identifier><identifier>PMID: 29980855</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Science + Business Media</publisher><subject>Agriculture ; Alleles ; Biological activity ; Biomedical and Life Sciences ; Chromosome Mapping ; Crop production ; Deoxyribonucleic acid ; DNA ; DNA sequencing ; Drought ; Drought resistance ; Droughts ; Ecology ; Forestry ; Gene frequency ; Gene mapping ; Genes ; Genetic diversity ; Genome-Wide Association Study ; Genotype ; Genotype & phenotype ; Genotypes ; Glycine max ; Glycine max - genetics ; Glycine max - physiology ; Inbreeding ; Life Sciences ; Linkage Disequilibrium ; Mapping ; Molecular chains ; Molecular Sequence Annotation ; Nucleotide sequence ; ORIGINAL ARTICLE ; Parents ; Phenotype ; Phenotypes ; Plant Sciences ; Polyethylene glycol ; Polyethylenes ; Polymorphism, Single Nucleotide - genetics ; Population ; Population genetics ; Quantitative trait loci ; Quantitative Trait Loci - genetics ; Seedlings ; Seedlings - genetics ; Seedlings - physiology ; Sequence Analysis, DNA ; Single-nucleotide polymorphism ; Soybeans</subject><ispartof>Planta, 2018-10, Vol.248 (4), p.947-962</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Planta is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c394t-f07bd94d8f83694e5b17b675e740348fc71cc991330a38cbfc474370ceef6bb43</citedby><cites>FETCH-LOGICAL-c394t-f07bd94d8f83694e5b17b675e740348fc71cc991330a38cbfc474370ceef6bb43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/48727035$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/48727035$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,27901,27902,41464,42533,51294,57992,58225</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29980855$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Khan, Mueen Alam</creatorcontrib><creatorcontrib>Tong, Fei</creatorcontrib><creatorcontrib>Wang, Wubin</creatorcontrib><creatorcontrib>He, Jianbo</creatorcontrib><creatorcontrib>Zhao, Tuanjie</creatorcontrib><creatorcontrib>Gai, Junyi</creatorcontrib><title>Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure</title><title>Planta</title><addtitle>Planta</addtitle><addtitle>Planta</addtitle><description>Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and Zheng-Yang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) = 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL–environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52–41.65 %), plus genetic variation (46.90–58.40%) in a total of 88.55–95.92% PV. The identified QTLs–alleles were organized into main-effect and QEI-effect QTL–allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098–1.132, transgressive over the parents (0.651–0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL–allele-based genomic selection for optimal cross selection.</description><subject>Agriculture</subject><subject>Alleles</subject><subject>Biological activity</subject><subject>Biomedical and Life Sciences</subject><subject>Chromosome Mapping</subject><subject>Crop production</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA sequencing</subject><subject>Drought</subject><subject>Drought resistance</subject><subject>Droughts</subject><subject>Ecology</subject><subject>Forestry</subject><subject>Gene frequency</subject><subject>Gene mapping</subject><subject>Genes</subject><subject>Genetic diversity</subject><subject>Genome-Wide Association Study</subject><subject>Genotype</subject><subject>Genotype & phenotype</subject><subject>Genotypes</subject><subject>Glycine max</subject><subject>Glycine max - genetics</subject><subject>Glycine max - physiology</subject><subject>Inbreeding</subject><subject>Life Sciences</subject><subject>Linkage Disequilibrium</subject><subject>Mapping</subject><subject>Molecular chains</subject><subject>Molecular Sequence Annotation</subject><subject>Nucleotide sequence</subject><subject>ORIGINAL ARTICLE</subject><subject>Parents</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Plant Sciences</subject><subject>Polyethylene glycol</subject><subject>Polyethylenes</subject><subject>Polymorphism, Single Nucleotide - genetics</subject><subject>Population</subject><subject>Population genetics</subject><subject>Quantitative trait loci</subject><subject>Quantitative Trait Loci - genetics</subject><subject>Seedlings</subject><subject>Seedlings - genetics</subject><subject>Seedlings - physiology</subject><subject>Sequence Analysis, DNA</subject><subject>Single-nucleotide polymorphism</subject><subject>Soybeans</subject><issn>0032-0935</issn><issn>1432-2048</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kU9u1DAYxS0EotPCAViALLEpiwz-GyfLUUUHpEEIUcQCochxvgwZeezUThDZcQduw3E4CQ4pRWLBypa_33vP9kPoESVrSoh6HgkRTGaEFhkrJcvEHbSigrOMEVHcRStC0p6UXJ6g0xgPhKShUvfRCSvLghRSrtCPjdN2il3EvsVvr3Y_v33X1oIFHKc4wBEb71oIoXN73AQ_7j8PePAWgnYGsB5wBGjsPI2D3gPuHNbYQZI2WMfoTaeHzjt81H0_U73vR7scpcDopxq0wx-3djKdg4R9xee79TP8OmWuP-ExzqLk6L-AxdsPm3e4D95AMwZ4gO612kZ4eLOeofeXL64uXma7N9tXF5tdZngphqwlqm5K0RRtwfNSgKypqnMlQQnCRdEaRY0pS8o50bwwdWuESt9EDECb17XgZ-h88U3J12N6WnXsogFrtQM_xoqRPBeFVDRP6NN_0IMfQ_rh35Skiio2U3ShTPAxBmirPnRHHaaKkmoutlqKrVKx1VxsNV_iyY3zWB-huVX8aTIBbAFiP5cF4W_0_1wfL6JDHHy4NRWFYopwyX8B1qK6ag</recordid><startdate>20181001</startdate><enddate>20181001</enddate><creator>Khan, Mueen Alam</creator><creator>Tong, Fei</creator><creator>Wang, Wubin</creator><creator>He, Jianbo</creator><creator>Zhao, Tuanjie</creator><creator>Gai, Junyi</creator><general>Springer Science + Business Media</general><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></search><sort><creationdate>20181001</creationdate><title>Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure</title><author>Khan, Mueen Alam ; Tong, Fei ; Wang, Wubin ; He, Jianbo ; Zhao, Tuanjie ; Gai, Junyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c394t-f07bd94d8f83694e5b17b675e740348fc71cc991330a38cbfc474370ceef6bb43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Agriculture</topic><topic>Alleles</topic><topic>Biological activity</topic><topic>Biomedical and Life Sciences</topic><topic>Chromosome Mapping</topic><topic>Crop production</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA sequencing</topic><topic>Drought</topic><topic>Drought resistance</topic><topic>Droughts</topic><topic>Ecology</topic><topic>Forestry</topic><topic>Gene frequency</topic><topic>Gene mapping</topic><topic>Genes</topic><topic>Genetic diversity</topic><topic>Genome-Wide Association Study</topic><topic>Genotype</topic><topic>Genotype & phenotype</topic><topic>Genotypes</topic><topic>Glycine max</topic><topic>Glycine max - genetics</topic><topic>Glycine max - physiology</topic><topic>Inbreeding</topic><topic>Life Sciences</topic><topic>Linkage Disequilibrium</topic><topic>Mapping</topic><topic>Molecular chains</topic><topic>Molecular Sequence Annotation</topic><topic>Nucleotide sequence</topic><topic>ORIGINAL ARTICLE</topic><topic>Parents</topic><topic>Phenotype</topic><topic>Phenotypes</topic><topic>Plant Sciences</topic><topic>Polyethylene glycol</topic><topic>Polyethylenes</topic><topic>Polymorphism, Single Nucleotide - genetics</topic><topic>Population</topic><topic>Population genetics</topic><topic>Quantitative trait loci</topic><topic>Quantitative Trait Loci - genetics</topic><topic>Seedlings</topic><topic>Seedlings - genetics</topic><topic>Seedlings - physiology</topic><topic>Sequence Analysis, DNA</topic><topic>Single-nucleotide polymorphism</topic><topic>Soybeans</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khan, Mueen Alam</creatorcontrib><creatorcontrib>Tong, Fei</creatorcontrib><creatorcontrib>Wang, Wubin</creatorcontrib><creatorcontrib>He, Jianbo</creatorcontrib><creatorcontrib>Zhao, Tuanjie</creatorcontrib><creatorcontrib>Gai, Junyi</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</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>Khan, Mueen Alam</au><au>Tong, Fei</au><au>Wang, Wubin</au><au>He, Jianbo</au><au>Zhao, Tuanjie</au><au>Gai, Junyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure</atitle><jtitle>Planta</jtitle><stitle>Planta</stitle><addtitle>Planta</addtitle><date>2018-10-01</date><risdate>2018</risdate><volume>248</volume><issue>4</issue><spage>947</spage><epage>962</epage><pages>947-962</pages><issn>0032-0935</issn><eissn>1432-2048</eissn><abstract>Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and Zheng-Yang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) = 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL–environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52–41.65 %), plus genetic variation (46.90–58.40%) in a total of 88.55–95.92% PV. The identified QTLs–alleles were organized into main-effect and QEI-effect QTL–allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098–1.132, transgressive over the parents (0.651–0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL–allele-based genomic selection for optimal cross selection.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Science + Business Media</pub><pmid>29980855</pmid><doi>10.1007/s00425-018-2952-4</doi><tpages>16</tpages></addata></record> |
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subjects | Agriculture Alleles Biological activity Biomedical and Life Sciences Chromosome Mapping Crop production Deoxyribonucleic acid DNA DNA sequencing Drought Drought resistance Droughts Ecology Forestry Gene frequency Gene mapping Genes Genetic diversity Genome-Wide Association Study Genotype Genotype & phenotype Genotypes Glycine max Glycine max - genetics Glycine max - physiology Inbreeding Life Sciences Linkage Disequilibrium Mapping Molecular chains Molecular Sequence Annotation Nucleotide sequence ORIGINAL ARTICLE Parents Phenotype Phenotypes Plant Sciences Polyethylene glycol Polyethylenes Polymorphism, Single Nucleotide - genetics Population Population genetics Quantitative trait loci Quantitative Trait Loci - genetics Seedlings Seedlings - genetics Seedlings - physiology Sequence Analysis, DNA Single-nucleotide polymorphism Soybeans |
title | Analysis of QTL–allele system conferring drought tolerance at seedling stage in a nested association mapping population of soybean [Glycine max (L.) Merr.] using a novel GWAS procedure |
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