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The development and use of a molecular model for soybean maturity groups
Achieving appropriate maturity in a target environment is essential to maximizing crop yield potential. In soybean [Glycine max (L.) Merr.], the time to maturity is largely dependent on developmental response to dark periods. Once the critical photoperiod is reached, flowering is initiated and repro...
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Published in: | BMC plant biology 2017-05, Vol.17 (1), p.91-91, Article 91 |
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description | Achieving appropriate maturity in a target environment is essential to maximizing crop yield potential. In soybean [Glycine max (L.) Merr.], the time to maturity is largely dependent on developmental response to dark periods. Once the critical photoperiod is reached, flowering is initiated and reproductive development proceeds. Therefore, soybean adaptation has been attributed to genetic changes and natural or artificial selection to optimize plant development in specific, narrow latitudinal ranges. In North America, these regions have been classified into twelve maturity groups (MG), with lower MG being shorter season than higher MG. Growing soybean lines not adapted to a particular environment typically results in poor growth and significant yield reductions. The objective of this study was to develop a molecular model for soybean maturity based on the alleles underlying the major maturity loci: E1, E2, and E3.
We determined the allelic variation and diversity of the E maturity genes in a large collection of soybean landraces, North American ancestors, Chinese cultivars, North American cultivars or expired Plant Variety Protection lines, and private-company lines. The E gene status of accessions in the USDA Soybean Germplasm Collection with SoySNP50K Beadchip data was also predicted. We determined the E allelic combinations needed to adapt soybean to different MGs in the United States (US) and discovered a strong signal of selection for E genotypes released in North America, particularly the US and Canada.
The E gene maturity model proposed will enable plant breeders to more effectively transfer traits into different MGs and increase the overall efficiency of soybean breeding in the US and Canada. The powerful yet simple selection strategy for increasing soybean breeding efficiency can be used alone or to directly enhance genomic prediction/selection schemes. The results also revealed previously unrecognized aspects of artificial selection in soybean imposed by soybean breeders based on geography that highlights the need for plant breeding that is optimized for specific environments. |
doi_str_mv | 10.1186/s12870-017-1040-4 |
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We determined the allelic variation and diversity of the E maturity genes in a large collection of soybean landraces, North American ancestors, Chinese cultivars, North American cultivars or expired Plant Variety Protection lines, and private-company lines. The E gene status of accessions in the USDA Soybean Germplasm Collection with SoySNP50K Beadchip data was also predicted. We determined the E allelic combinations needed to adapt soybean to different MGs in the United States (US) and discovered a strong signal of selection for E genotypes released in North America, particularly the US and Canada.
The E gene maturity model proposed will enable plant breeders to more effectively transfer traits into different MGs and increase the overall efficiency of soybean breeding in the US and Canada. The powerful yet simple selection strategy for increasing soybean breeding efficiency can be used alone or to directly enhance genomic prediction/selection schemes. The results also revealed previously unrecognized aspects of artificial selection in soybean imposed by soybean breeders based on geography that highlights the need for plant breeding that is optimized for specific environments.</description><identifier>ISSN: 1471-2229</identifier><identifier>EISSN: 1471-2229</identifier><identifier>DOI: 10.1186/s12870-017-1040-4</identifier><identifier>PMID: 28558691</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Adaptation ; Collection ; Crop yield ; Crops ; Cultivars ; E genes ; E protein ; Efficiency ; Environments ; Flowering ; Genes ; Genes, Plant ; Genetic diversity ; Genomics ; Genotypes ; Geography ; Germplasm ; Glycine ; Glycine max ; Glycine max - growth & development ; Loci ; Mathematical models ; Maturity ; Maturity group ; Models, Genetic ; Mutation ; Plant breeding ; Plant protection ; Predictions ; Seed Bank ; Selection, Genetic ; Soybean ; Soybeans ; Transcription</subject><ispartof>BMC plant biology, 2017-05, Vol.17 (1), p.91-91, Article 91</ispartof><rights>Copyright BioMed Central 2017</rights><rights>The Author(s). 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c559t-251947c7db26b5c792962c5ecaacdba8b950c6494d29fdb8ff9a5ad6c3981f743</citedby><cites>FETCH-LOGICAL-c559t-251947c7db26b5c792962c5ecaacdba8b950c6494d29fdb8ff9a5ad6c3981f743</cites><orcidid>0000-0002-4141-4790</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5450301/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1904976131?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28558691$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Langewisch, Tiffany</creatorcontrib><creatorcontrib>Lenis, Julian</creatorcontrib><creatorcontrib>Jiang, Guo-Liang</creatorcontrib><creatorcontrib>Wang, Dechun</creatorcontrib><creatorcontrib>Pantalone, Vince</creatorcontrib><creatorcontrib>Bilyeu, Kristin</creatorcontrib><title>The development and use of a molecular model for soybean maturity groups</title><title>BMC plant biology</title><addtitle>BMC Plant Biol</addtitle><description>Achieving appropriate maturity in a target environment is essential to maximizing crop yield potential. In soybean [Glycine max (L.) Merr.], the time to maturity is largely dependent on developmental response to dark periods. Once the critical photoperiod is reached, flowering is initiated and reproductive development proceeds. Therefore, soybean adaptation has been attributed to genetic changes and natural or artificial selection to optimize plant development in specific, narrow latitudinal ranges. In North America, these regions have been classified into twelve maturity groups (MG), with lower MG being shorter season than higher MG. Growing soybean lines not adapted to a particular environment typically results in poor growth and significant yield reductions. The objective of this study was to develop a molecular model for soybean maturity based on the alleles underlying the major maturity loci: E1, E2, and E3.
We determined the allelic variation and diversity of the E maturity genes in a large collection of soybean landraces, North American ancestors, Chinese cultivars, North American cultivars or expired Plant Variety Protection lines, and private-company lines. The E gene status of accessions in the USDA Soybean Germplasm Collection with SoySNP50K Beadchip data was also predicted. We determined the E allelic combinations needed to adapt soybean to different MGs in the United States (US) and discovered a strong signal of selection for E genotypes released in North America, particularly the US and Canada.
The E gene maturity model proposed will enable plant breeders to more effectively transfer traits into different MGs and increase the overall efficiency of soybean breeding in the US and Canada. The powerful yet simple selection strategy for increasing soybean breeding efficiency can be used alone or to directly enhance genomic prediction/selection schemes. The results also revealed previously unrecognized aspects of artificial selection in soybean imposed by soybean breeders based on geography that highlights the need for plant breeding that is optimized for specific environments.</description><subject>Adaptation</subject><subject>Collection</subject><subject>Crop yield</subject><subject>Crops</subject><subject>Cultivars</subject><subject>E genes</subject><subject>E protein</subject><subject>Efficiency</subject><subject>Environments</subject><subject>Flowering</subject><subject>Genes</subject><subject>Genes, Plant</subject><subject>Genetic diversity</subject><subject>Genomics</subject><subject>Genotypes</subject><subject>Geography</subject><subject>Germplasm</subject><subject>Glycine</subject><subject>Glycine max</subject><subject>Glycine max - growth & development</subject><subject>Loci</subject><subject>Mathematical models</subject><subject>Maturity</subject><subject>Maturity group</subject><subject>Models, Genetic</subject><subject>Mutation</subject><subject>Plant breeding</subject><subject>Plant protection</subject><subject>Predictions</subject><subject>Seed Bank</subject><subject>Selection, Genetic</subject><subject>Soybean</subject><subject>Soybeans</subject><subject>Transcription</subject><issn>1471-2229</issn><issn>1471-2229</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkU1v1DAQhiMEoqXwA7ggS1y4BDyOHXsuSKiCtlIlLuVs-XOblRMvdlJp_z3Zblu1nDyyHz-ambdpPgL9CqD6bxWYkrSlIFugnLb8VXMKXELLGMPXz-qT5l2tW7qCiuPb5oQpIVSPcNpc3twG4sNdSHk3hmkmZvJkqYHkSAwZcwpuSaaslQ-JxFxIzXsbzERGMy9lmPdkU_Kyq--bN9GkGj48nGfNn18_b84v2-vfF1fnP65bJwTOLROAXDrpLeutcBIZ9syJ4Ixx3hplUVDXc-SeYfRWxYhGGN-7DhVEybuz5uro9dls9a4Moyl7nc2g7y9y2WhT5sGloJ1xGIF5jK7jnHmLqBwN1DJlOx7t6vp-dO0WOwbv1vmLSS-kL1-m4VZv8p0WXNCOwir48iAo-e8S6qzHobqQkplCXqoGpJx1CJKu6Of_0G1eyrSu6p5C2UN3EMKRciXXWkJ8agaoPmSuj5nrNUp9yFwfNvLp-RRPPx5D7v4BjxuoOQ</recordid><startdate>20170530</startdate><enddate>20170530</enddate><creator>Langewisch, Tiffany</creator><creator>Lenis, Julian</creator><creator>Jiang, Guo-Liang</creator><creator>Wang, Dechun</creator><creator>Pantalone, Vince</creator><creator>Bilyeu, Kristin</creator><general>BioMed Central</general><general>BMC</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>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>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>M7N</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4141-4790</orcidid></search><sort><creationdate>20170530</creationdate><title>The development and use of a molecular model for soybean maturity groups</title><author>Langewisch, Tiffany ; Lenis, Julian ; Jiang, Guo-Liang ; Wang, Dechun ; Pantalone, Vince ; Bilyeu, Kristin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c559t-251947c7db26b5c792962c5ecaacdba8b950c6494d29fdb8ff9a5ad6c3981f743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptation</topic><topic>Collection</topic><topic>Crop yield</topic><topic>Crops</topic><topic>Cultivars</topic><topic>E genes</topic><topic>E protein</topic><topic>Efficiency</topic><topic>Environments</topic><topic>Flowering</topic><topic>Genes</topic><topic>Genes, Plant</topic><topic>Genetic diversity</topic><topic>Genomics</topic><topic>Genotypes</topic><topic>Geography</topic><topic>Germplasm</topic><topic>Glycine</topic><topic>Glycine max</topic><topic>Glycine max - growth & development</topic><topic>Loci</topic><topic>Mathematical models</topic><topic>Maturity</topic><topic>Maturity group</topic><topic>Models, Genetic</topic><topic>Mutation</topic><topic>Plant breeding</topic><topic>Plant protection</topic><topic>Predictions</topic><topic>Seed Bank</topic><topic>Selection, Genetic</topic><topic>Soybean</topic><topic>Soybeans</topic><topic>Transcription</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Langewisch, Tiffany</creatorcontrib><creatorcontrib>Lenis, Julian</creatorcontrib><creatorcontrib>Jiang, Guo-Liang</creatorcontrib><creatorcontrib>Wang, Dechun</creatorcontrib><creatorcontrib>Pantalone, Vince</creatorcontrib><creatorcontrib>Bilyeu, Kristin</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>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>BMC plant biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Langewisch, Tiffany</au><au>Lenis, Julian</au><au>Jiang, Guo-Liang</au><au>Wang, Dechun</au><au>Pantalone, Vince</au><au>Bilyeu, Kristin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The development and use of a molecular model for soybean maturity groups</atitle><jtitle>BMC plant biology</jtitle><addtitle>BMC Plant Biol</addtitle><date>2017-05-30</date><risdate>2017</risdate><volume>17</volume><issue>1</issue><spage>91</spage><epage>91</epage><pages>91-91</pages><artnum>91</artnum><issn>1471-2229</issn><eissn>1471-2229</eissn><abstract>Achieving appropriate maturity in a target environment is essential to maximizing crop yield potential. In soybean [Glycine max (L.) Merr.], the time to maturity is largely dependent on developmental response to dark periods. Once the critical photoperiod is reached, flowering is initiated and reproductive development proceeds. Therefore, soybean adaptation has been attributed to genetic changes and natural or artificial selection to optimize plant development in specific, narrow latitudinal ranges. In North America, these regions have been classified into twelve maturity groups (MG), with lower MG being shorter season than higher MG. Growing soybean lines not adapted to a particular environment typically results in poor growth and significant yield reductions. The objective of this study was to develop a molecular model for soybean maturity based on the alleles underlying the major maturity loci: E1, E2, and E3.
We determined the allelic variation and diversity of the E maturity genes in a large collection of soybean landraces, North American ancestors, Chinese cultivars, North American cultivars or expired Plant Variety Protection lines, and private-company lines. The E gene status of accessions in the USDA Soybean Germplasm Collection with SoySNP50K Beadchip data was also predicted. We determined the E allelic combinations needed to adapt soybean to different MGs in the United States (US) and discovered a strong signal of selection for E genotypes released in North America, particularly the US and Canada.
The E gene maturity model proposed will enable plant breeders to more effectively transfer traits into different MGs and increase the overall efficiency of soybean breeding in the US and Canada. The powerful yet simple selection strategy for increasing soybean breeding efficiency can be used alone or to directly enhance genomic prediction/selection schemes. The results also revealed previously unrecognized aspects of artificial selection in soybean imposed by soybean breeders based on geography that highlights the need for plant breeding that is optimized for specific environments.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>28558691</pmid><doi>10.1186/s12870-017-1040-4</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-4141-4790</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptation Collection Crop yield Crops Cultivars E genes E protein Efficiency Environments Flowering Genes Genes, Plant Genetic diversity Genomics Genotypes Geography Germplasm Glycine Glycine max Glycine max - growth & development Loci Mathematical models Maturity Maturity group Models, Genetic Mutation Plant breeding Plant protection Predictions Seed Bank Selection, Genetic Soybean Soybeans Transcription |
title | The development and use of a molecular model for soybean maturity groups |
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