An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding
Key message The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding. The previous genome-wide association studies (...
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| Veröffentlicht in: | Theoretical and applied genetics 2017-11, Vol.130 (11), p.2327-2343 |
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| creator | He, Jianbo Meng, Shan Zhao, Tuanjie Xing, Guangnan Yang, Shouping Li, Yan Guan, Rongzhan Lu, Jiangjie Wang, Yufeng Xia, Qiuju Yang, Bing Gai, Junyi |
| description | Key message
The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding.
The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS,
https://github.com/njau-sri/rtm-gwas
). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population. |
| doi_str_mv | 10.1007/s00122-017-2962-9 |
| format | Article |
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The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding.
The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS,
https://github.com/njau-sri/rtm-gwas
). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.</description><identifier>ISSN: 0040-5752</identifier><identifier>EISSN: 1432-2242</identifier><identifier>DOI: 10.1007/s00122-017-2962-9</identifier><identifier>PMID: 28828506</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agriculture ; Alleles ; Association analysis ; Biochemistry ; Biomedical and Life Sciences ; Biotechnology ; Computer Simulation ; Constitution ; Genetic aspects ; Genetic Association Studies - methods ; Genetic Markers ; Genetics, Population ; Genome-wide association studies ; Genomes ; Genotype & phenotype ; Germplasm ; Glycine max - genetics ; Growth ; Haplotypes ; Heritability ; Innovations ; Life Sciences ; Linkage Disequilibrium ; Models, Genetic ; Original Article ; Phenotype ; Plant Biochemistry ; Plant Breeding ; Plant Breeding/Biotechnology ; Plant Genetics and Genomics ; Polymorphism, Single Nucleotide ; Population ; Population genetics ; Population studies ; Power efficiency ; Quantitative Trait Loci ; Seeds ; Selection, Genetic ; Single-nucleotide polymorphism ; Soybeans</subject><ispartof>Theoretical and applied genetics, 2017-11, Vol.130 (11), p.2327-2343</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>COPYRIGHT 2017 Springer</rights><rights>Theoretical and Applied Genetics is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c473t-462561ef291a3509ab354faa1ec66f57b76605db03eb4bd28ba8d6eb5f717d223</citedby><cites>FETCH-LOGICAL-c473t-462561ef291a3509ab354faa1ec66f57b76605db03eb4bd28ba8d6eb5f717d223</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00122-017-2962-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00122-017-2962-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28828506$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>He, Jianbo</creatorcontrib><creatorcontrib>Meng, Shan</creatorcontrib><creatorcontrib>Zhao, Tuanjie</creatorcontrib><creatorcontrib>Xing, Guangnan</creatorcontrib><creatorcontrib>Yang, Shouping</creatorcontrib><creatorcontrib>Li, Yan</creatorcontrib><creatorcontrib>Guan, Rongzhan</creatorcontrib><creatorcontrib>Lu, Jiangjie</creatorcontrib><creatorcontrib>Wang, Yufeng</creatorcontrib><creatorcontrib>Xia, Qiuju</creatorcontrib><creatorcontrib>Yang, Bing</creatorcontrib><creatorcontrib>Gai, Junyi</creatorcontrib><title>An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding</title><title>Theoretical and applied genetics</title><addtitle>Theor Appl Genet</addtitle><addtitle>Theor Appl Genet</addtitle><description>Key message
The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding.
The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS,
https://github.com/njau-sri/rtm-gwas
). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.</description><subject>Agriculture</subject><subject>Alleles</subject><subject>Association analysis</subject><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biotechnology</subject><subject>Computer Simulation</subject><subject>Constitution</subject><subject>Genetic aspects</subject><subject>Genetic Association Studies - methods</subject><subject>Genetic Markers</subject><subject>Genetics, Population</subject><subject>Genome-wide association studies</subject><subject>Genomes</subject><subject>Genotype & phenotype</subject><subject>Germplasm</subject><subject>Glycine max - genetics</subject><subject>Growth</subject><subject>Haplotypes</subject><subject>Heritability</subject><subject>Innovations</subject><subject>Life Sciences</subject><subject>Linkage Disequilibrium</subject><subject>Models, Genetic</subject><subject>Original Article</subject><subject>Phenotype</subject><subject>Plant Biochemistry</subject><subject>Plant Breeding</subject><subject>Plant Breeding/Biotechnology</subject><subject>Plant Genetics and Genomics</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Population</subject><subject>Population genetics</subject><subject>Population studies</subject><subject>Power efficiency</subject><subject>Quantitative Trait Loci</subject><subject>Seeds</subject><subject>Selection, Genetic</subject><subject>Single-nucleotide polymorphism</subject><subject>Soybeans</subject><issn>0040-5752</issn><issn>1432-2242</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kktv1TAQhSMEopfCD2CDLLGBRYofsZMsryoKlSoh8VhbTjyOXCV28CSF_vs6ui1wEcgLSzPfGc3RnKJ4yegZo7R-h5QyzkvK6pK3ipfto2LHKsFLziv-uNhRWtFS1pKfFM8QrymlXFLxtDjhTcMbSdWuwH0gPoR4YxZ_A2ROsQe7JiDRkQFCnKD84S0Qgxh7n6EYiAlmvEWPxPkFCS6r9YAkNwZI0zwanMgc53V8oC3JxbCQLgFYH4bnxRNnRoQX9_9p8e3i_dfzj-XVpw-X5_ursq9qsZSV4lIxcLxlRkjamk7IyhnDoFfKybqrlaLSdlRAV3WWN51prIJOuprVlnNxWrw5zM2uvq-Ai5489jDmZSCuqFkrGK8kU21GX_-FXsc1ZZ8bJWlDMyR_U4MZQfvg4pJMvw3V-7ygEo1oq0yd_YPKz8Lk-xjA-Vw_Erw9EmRmgZ_LYFZEffnl8zHLDmyfImICp-fkJ5NuNaN6C4U-hELnUOgtFHoz9-re3NpNYH8pHlKQAX4AMLdCvuIf7v879Q6xNMCv</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>He, Jianbo</creator><creator>Meng, Shan</creator><creator>Zhao, Tuanjie</creator><creator>Xing, Guangnan</creator><creator>Yang, Shouping</creator><creator>Li, Yan</creator><creator>Guan, Rongzhan</creator><creator>Lu, Jiangjie</creator><creator>Wang, Yufeng</creator><creator>Xia, Qiuju</creator><creator>Yang, Bing</creator><creator>Gai, Junyi</creator><general>Springer Berlin Heidelberg</general><general>Springer</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>ISR</scope><scope>3V.</scope><scope>7SS</scope><scope>7TK</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>AFKRA</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>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20171101</creationdate><title>An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding</title><author>He, Jianbo ; Meng, Shan ; Zhao, Tuanjie ; Xing, Guangnan ; Yang, Shouping ; Li, Yan ; Guan, Rongzhan ; Lu, Jiangjie ; Wang, Yufeng ; Xia, Qiuju ; Yang, Bing ; Gai, Junyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c473t-462561ef291a3509ab354faa1ec66f57b76605db03eb4bd28ba8d6eb5f717d223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Agriculture</topic><topic>Alleles</topic><topic>Association analysis</topic><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biotechnology</topic><topic>Computer Simulation</topic><topic>Constitution</topic><topic>Genetic aspects</topic><topic>Genetic Association Studies - methods</topic><topic>Genetic Markers</topic><topic>Genetics, Population</topic><topic>Genome-wide association studies</topic><topic>Genomes</topic><topic>Genotype & phenotype</topic><topic>Germplasm</topic><topic>Glycine max - genetics</topic><topic>Growth</topic><topic>Haplotypes</topic><topic>Heritability</topic><topic>Innovations</topic><topic>Life Sciences</topic><topic>Linkage Disequilibrium</topic><topic>Models, Genetic</topic><topic>Original Article</topic><topic>Phenotype</topic><topic>Plant Biochemistry</topic><topic>Plant Breeding</topic><topic>Plant Breeding/Biotechnology</topic><topic>Plant Genetics and Genomics</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Population</topic><topic>Population genetics</topic><topic>Population studies</topic><topic>Power efficiency</topic><topic>Quantitative Trait Loci</topic><topic>Seeds</topic><topic>Selection, Genetic</topic><topic>Single-nucleotide polymorphism</topic><topic>Soybeans</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Jianbo</creatorcontrib><creatorcontrib>Meng, Shan</creatorcontrib><creatorcontrib>Zhao, Tuanjie</creatorcontrib><creatorcontrib>Xing, Guangnan</creatorcontrib><creatorcontrib>Yang, Shouping</creatorcontrib><creatorcontrib>Li, Yan</creatorcontrib><creatorcontrib>Guan, Rongzhan</creatorcontrib><creatorcontrib>Lu, Jiangjie</creatorcontrib><creatorcontrib>Wang, Yufeng</creatorcontrib><creatorcontrib>Xia, Qiuju</creatorcontrib><creatorcontrib>Yang, Bing</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>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</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 Central UK/Ireland</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>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>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Theoretical and applied genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>He, Jianbo</au><au>Meng, Shan</au><au>Zhao, Tuanjie</au><au>Xing, Guangnan</au><au>Yang, Shouping</au><au>Li, Yan</au><au>Guan, Rongzhan</au><au>Lu, Jiangjie</au><au>Wang, Yufeng</au><au>Xia, Qiuju</au><au>Yang, Bing</au><au>Gai, Junyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding</atitle><jtitle>Theoretical and applied genetics</jtitle><stitle>Theor Appl Genet</stitle><addtitle>Theor Appl Genet</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>130</volume><issue>11</issue><spage>2327</spage><epage>2343</epage><pages>2327-2343</pages><issn>0040-5752</issn><eissn>1432-2242</eissn><abstract>Key message
The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding.
The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS,
https://github.com/njau-sri/rtm-gwas
). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>28828506</pmid><doi>10.1007/s00122-017-2962-9</doi><tpages>17</tpages></addata></record> |
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| subjects | Agriculture Alleles Association analysis Biochemistry Biomedical and Life Sciences Biotechnology Computer Simulation Constitution Genetic aspects Genetic Association Studies - methods Genetic Markers Genetics, Population Genome-wide association studies Genomes Genotype & phenotype Germplasm Glycine max - genetics Growth Haplotypes Heritability Innovations Life Sciences Linkage Disequilibrium Models, Genetic Original Article Phenotype Plant Biochemistry Plant Breeding Plant Breeding/Biotechnology Plant Genetics and Genomics Polymorphism, Single Nucleotide Population Population genetics Population studies Power efficiency Quantitative Trait Loci Seeds Selection, Genetic Single-nucleotide polymorphism Soybeans |
| title | An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding |
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