Genome-wide association mapping of agronomic traits and carbon isotope discrimination in a worldwide germplasm collection of spring wheat using SNP markers
Association mapping has been proposed to identify polymorphisms involved in phenotypic variations and may prove useful in identifying interesting alleles for breeding purposes. Using this approach, a total of 382 cultivars and advanced lines of spring wheat obtained from three breeding programs (Chi...
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description | Association mapping has been proposed to identify polymorphisms involved in phenotypic variations and may prove useful in identifying interesting alleles for breeding purposes. Using this approach, a total of 382 cultivars and advanced lines of spring wheat obtained from three breeding programs (Chile, Uruguay and CIMMYT) were evaluated for plant height (PH), kernels per spike (KS), 1,000 kernel weight (TKW), grain yield and carbon isotope discrimination (Δ
13
C) and tested for genotyping-by-sequencing-derived SNP markers across the hexaploid wheat genome. A Bayesian clustering approach via Markov chain Monte Carlo was performed to examine the genetic differentiation (
F
ST
) among different genetic groups. The results indicated the existence of two distinct and strongly differentiated genetic groups. Cluster I contained 215 genotypes (56.3 %), over 60 % (137/215) of which were collected from CIMMYT. Cluster II showed the highest
F
ST
value, according to 95 % credible interval. Linkage disequilibrium (LD) among SNPs was calculated for the A, B and D genomes and at the whole-genome level. LD decayed over a longer genetic distance for the D genome than for the A and B genomes. In the A and B genomes, LD declined to 50 % of its initial value at about 2 cM. In the D genome, LD was much more extensive, declining to 50 % of its initial value only at 22 cM. In the whole genome, LD declined to 50 % of its initial value at an average of 4 cM. Important genomic regions associated with complex traits in spring wheat were identified. Selection on these regions may increase the efficiency of the current breeding programs. Although most of the associations were environment specific, some stable associations were detected for Δ
13
C, KS, PH and TKW. Chromosomes 1A, 3A, 4A and 5A were the most important chromosomes, as they comprised quantitative trait loci (QTL) for Δ
13
C, a trait that can be used as an indirect tool for increased water-use efficiency in wheat. Environment-specific genomic regions were detected, indicating the presence of QTL-by-environment interaction. To produce suitable genotypes under contrasting water availability conditions, QTL × E interactions (and genotype-by-environment interaction) should be considered in the current spring wheat breeding program. |
doi_str_mv | 10.1007/s11032-015-0264-y |
format | Article |
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13
C) and tested for genotyping-by-sequencing-derived SNP markers across the hexaploid wheat genome. A Bayesian clustering approach via Markov chain Monte Carlo was performed to examine the genetic differentiation (
F
ST
) among different genetic groups. The results indicated the existence of two distinct and strongly differentiated genetic groups. Cluster I contained 215 genotypes (56.3 %), over 60 % (137/215) of which were collected from CIMMYT. Cluster II showed the highest
F
ST
value, according to 95 % credible interval. Linkage disequilibrium (LD) among SNPs was calculated for the A, B and D genomes and at the whole-genome level. LD decayed over a longer genetic distance for the D genome than for the A and B genomes. In the A and B genomes, LD declined to 50 % of its initial value at about 2 cM. In the D genome, LD was much more extensive, declining to 50 % of its initial value only at 22 cM. In the whole genome, LD declined to 50 % of its initial value at an average of 4 cM. Important genomic regions associated with complex traits in spring wheat were identified. Selection on these regions may increase the efficiency of the current breeding programs. Although most of the associations were environment specific, some stable associations were detected for Δ
13
C, KS, PH and TKW. Chromosomes 1A, 3A, 4A and 5A were the most important chromosomes, as they comprised quantitative trait loci (QTL) for Δ
13
C, a trait that can be used as an indirect tool for increased water-use efficiency in wheat. Environment-specific genomic regions were detected, indicating the presence of QTL-by-environment interaction. To produce suitable genotypes under contrasting water availability conditions, QTL × E interactions (and genotype-by-environment interaction) should be considered in the current spring wheat breeding program.</description><identifier>ISSN: 1380-3743</identifier><identifier>EISSN: 1572-9788</identifier><identifier>DOI: 10.1007/s11032-015-0264-y</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Agronomy ; Bayesian analysis ; Biomedical and Life Sciences ; Biotechnology ; Carbon isotopes ; Chromosomes ; Clustering ; Computer simulation ; Crop yield ; Cultivars ; Gene mapping ; Gene sequencing ; Genetic distance ; Genomes ; Genomics ; Genotype-environment interactions ; Genotypes ; Genotyping ; Germplasm ; Kernels ; Life Sciences ; Linkage disequilibrium ; Mapping ; Markers ; Markov chains ; Molecular biology ; Phenotypic variations ; Plant biology ; Plant breeding ; Plant Genetics and Genomics ; Plant Pathology ; Plant Physiology ; Plant Sciences ; Quantitative trait loci ; Single-nucleotide polymorphism ; Spring wheat ; Water availability ; Water use ; Wheat</subject><ispartof>Molecular breeding, 2015-02, Vol.35 (2), p.1-12, Article 69</ispartof><rights>Springer Science+Business Media Dordrecht 2015</rights><rights>Molecular Breeding is a copyright of Springer, (2015). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-439ca69990d2986b49944c73983295f0600db9e20b3940da7cba83d121bdb1d43</citedby><cites>FETCH-LOGICAL-c316t-439ca69990d2986b49944c73983295f0600db9e20b3940da7cba83d121bdb1d43</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/s11032-015-0264-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11032-015-0264-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids></links><search><creatorcontrib>Mora, Freddy</creatorcontrib><creatorcontrib>Castillo, Dalma</creatorcontrib><creatorcontrib>Lado, Bettina</creatorcontrib><creatorcontrib>Matus, Ivan</creatorcontrib><creatorcontrib>Poland, Jesse</creatorcontrib><creatorcontrib>Belzile, François</creatorcontrib><creatorcontrib>von Zitzewitz, Jarislav</creatorcontrib><creatorcontrib>del Pozo, Alejandro</creatorcontrib><title>Genome-wide association mapping of agronomic traits and carbon isotope discrimination in a worldwide germplasm collection of spring wheat using SNP markers</title><title>Molecular breeding</title><addtitle>Mol Breeding</addtitle><description>Association mapping has been proposed to identify polymorphisms involved in phenotypic variations and may prove useful in identifying interesting alleles for breeding purposes. Using this approach, a total of 382 cultivars and advanced lines of spring wheat obtained from three breeding programs (Chile, Uruguay and CIMMYT) were evaluated for plant height (PH), kernels per spike (KS), 1,000 kernel weight (TKW), grain yield and carbon isotope discrimination (Δ
13
C) and tested for genotyping-by-sequencing-derived SNP markers across the hexaploid wheat genome. A Bayesian clustering approach via Markov chain Monte Carlo was performed to examine the genetic differentiation (
F
ST
) among different genetic groups. The results indicated the existence of two distinct and strongly differentiated genetic groups. Cluster I contained 215 genotypes (56.3 %), over 60 % (137/215) of which were collected from CIMMYT. Cluster II showed the highest
F
ST
value, according to 95 % credible interval. Linkage disequilibrium (LD) among SNPs was calculated for the A, B and D genomes and at the whole-genome level. LD decayed over a longer genetic distance for the D genome than for the A and B genomes. In the A and B genomes, LD declined to 50 % of its initial value at about 2 cM. In the D genome, LD was much more extensive, declining to 50 % of its initial value only at 22 cM. In the whole genome, LD declined to 50 % of its initial value at an average of 4 cM. Important genomic regions associated with complex traits in spring wheat were identified. Selection on these regions may increase the efficiency of the current breeding programs. Although most of the associations were environment specific, some stable associations were detected for Δ
13
C, KS, PH and TKW. Chromosomes 1A, 3A, 4A and 5A were the most important chromosomes, as they comprised quantitative trait loci (QTL) for Δ
13
C, a trait that can be used as an indirect tool for increased water-use efficiency in wheat. Environment-specific genomic regions were detected, indicating the presence of QTL-by-environment interaction. To produce suitable genotypes under contrasting water availability conditions, QTL × E interactions (and genotype-by-environment interaction) should be considered in the current spring wheat breeding program.</description><subject>Agronomy</subject><subject>Bayesian analysis</subject><subject>Biomedical and Life Sciences</subject><subject>Biotechnology</subject><subject>Carbon isotopes</subject><subject>Chromosomes</subject><subject>Clustering</subject><subject>Computer simulation</subject><subject>Crop yield</subject><subject>Cultivars</subject><subject>Gene mapping</subject><subject>Gene sequencing</subject><subject>Genetic distance</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Genotype-environment interactions</subject><subject>Genotypes</subject><subject>Genotyping</subject><subject>Germplasm</subject><subject>Kernels</subject><subject>Life Sciences</subject><subject>Linkage disequilibrium</subject><subject>Mapping</subject><subject>Markers</subject><subject>Markov chains</subject><subject>Molecular biology</subject><subject>Phenotypic variations</subject><subject>Plant biology</subject><subject>Plant breeding</subject><subject>Plant Genetics and Genomics</subject><subject>Plant Pathology</subject><subject>Plant Physiology</subject><subject>Plant Sciences</subject><subject>Quantitative trait loci</subject><subject>Single-nucleotide polymorphism</subject><subject>Spring wheat</subject><subject>Water availability</subject><subject>Water use</subject><subject>Wheat</subject><issn>1380-3743</issn><issn>1572-9788</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kc1KAzEUhYMoWKsP4C7gOpq_-clSilahqKCuQybJ1NSZyZhMKX0WX9ZMR3Dl6t7F-c65lwPAJcHXBOPiJhKCGUWYZAjTnKP9EZiRrKBIFGV5nHZWYsQKzk7BWYwbnBiR5zPwvbSdby3aOWOhitFrpwbnO9iqvnfdGvoaqnXwSeQ0HIJyQ4SqM1CrUCWZi37wvYXGRR1c67qJdh1UcOdDYw7GaxvavlGxhdo3jdUHTXKOfRgzdh9WDXAbx_316SVlh08b4jk4qVUT7cXvnIP3-7u3xQNaPS8fF7crpBnJB8SZ0CoXQmBDRZlXXAjOdcFEyajIapxjbCphKa6Y4NioQleqZIZQUpmKGM7m4Gry7YP_2to4yI3fhi5FSkozwQnJk9cckEmlg48x2Fqm49Ole0mwHDuQUwcydSDHDuQ-MXRipkdt-HP-H_oBefWNHQ</recordid><startdate>20150201</startdate><enddate>20150201</enddate><creator>Mora, Freddy</creator><creator>Castillo, Dalma</creator><creator>Lado, Bettina</creator><creator>Matus, Ivan</creator><creator>Poland, Jesse</creator><creator>Belzile, François</creator><creator>von Zitzewitz, Jarislav</creator><creator>del Pozo, Alejandro</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X2</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</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>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M0K</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20150201</creationdate><title>Genome-wide association mapping of agronomic traits and carbon isotope discrimination in a worldwide germplasm collection of spring wheat using SNP markers</title><author>Mora, Freddy ; Castillo, Dalma ; Lado, Bettina ; Matus, Ivan ; Poland, Jesse ; Belzile, François ; von Zitzewitz, Jarislav ; del Pozo, Alejandro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-439ca69990d2986b49944c73983295f0600db9e20b3940da7cba83d121bdb1d43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Agronomy</topic><topic>Bayesian analysis</topic><topic>Biomedical and Life Sciences</topic><topic>Biotechnology</topic><topic>Carbon isotopes</topic><topic>Chromosomes</topic><topic>Clustering</topic><topic>Computer simulation</topic><topic>Crop yield</topic><topic>Cultivars</topic><topic>Gene mapping</topic><topic>Gene sequencing</topic><topic>Genetic distance</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Genotype-environment interactions</topic><topic>Genotypes</topic><topic>Genotyping</topic><topic>Germplasm</topic><topic>Kernels</topic><topic>Life Sciences</topic><topic>Linkage disequilibrium</topic><topic>Mapping</topic><topic>Markers</topic><topic>Markov chains</topic><topic>Molecular biology</topic><topic>Phenotypic variations</topic><topic>Plant biology</topic><topic>Plant breeding</topic><topic>Plant Genetics and Genomics</topic><topic>Plant Pathology</topic><topic>Plant Physiology</topic><topic>Plant Sciences</topic><topic>Quantitative trait loci</topic><topic>Single-nucleotide polymorphism</topic><topic>Spring wheat</topic><topic>Water availability</topic><topic>Water use</topic><topic>Wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mora, Freddy</creatorcontrib><creatorcontrib>Castillo, Dalma</creatorcontrib><creatorcontrib>Lado, Bettina</creatorcontrib><creatorcontrib>Matus, Ivan</creatorcontrib><creatorcontrib>Poland, Jesse</creatorcontrib><creatorcontrib>Belzile, François</creatorcontrib><creatorcontrib>von Zitzewitz, Jarislav</creatorcontrib><creatorcontrib>del Pozo, Alejandro</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</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>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Biological Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Molecular breeding</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mora, Freddy</au><au>Castillo, Dalma</au><au>Lado, Bettina</au><au>Matus, Ivan</au><au>Poland, Jesse</au><au>Belzile, François</au><au>von Zitzewitz, Jarislav</au><au>del Pozo, Alejandro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genome-wide association mapping of agronomic traits and carbon isotope discrimination in a worldwide germplasm collection of spring wheat using SNP markers</atitle><jtitle>Molecular breeding</jtitle><stitle>Mol Breeding</stitle><date>2015-02-01</date><risdate>2015</risdate><volume>35</volume><issue>2</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><artnum>69</artnum><issn>1380-3743</issn><eissn>1572-9788</eissn><abstract>Association mapping has been proposed to identify polymorphisms involved in phenotypic variations and may prove useful in identifying interesting alleles for breeding purposes. Using this approach, a total of 382 cultivars and advanced lines of spring wheat obtained from three breeding programs (Chile, Uruguay and CIMMYT) were evaluated for plant height (PH), kernels per spike (KS), 1,000 kernel weight (TKW), grain yield and carbon isotope discrimination (Δ
13
C) and tested for genotyping-by-sequencing-derived SNP markers across the hexaploid wheat genome. A Bayesian clustering approach via Markov chain Monte Carlo was performed to examine the genetic differentiation (
F
ST
) among different genetic groups. The results indicated the existence of two distinct and strongly differentiated genetic groups. Cluster I contained 215 genotypes (56.3 %), over 60 % (137/215) of which were collected from CIMMYT. Cluster II showed the highest
F
ST
value, according to 95 % credible interval. Linkage disequilibrium (LD) among SNPs was calculated for the A, B and D genomes and at the whole-genome level. LD decayed over a longer genetic distance for the D genome than for the A and B genomes. In the A and B genomes, LD declined to 50 % of its initial value at about 2 cM. In the D genome, LD was much more extensive, declining to 50 % of its initial value only at 22 cM. In the whole genome, LD declined to 50 % of its initial value at an average of 4 cM. Important genomic regions associated with complex traits in spring wheat were identified. Selection on these regions may increase the efficiency of the current breeding programs. Although most of the associations were environment specific, some stable associations were detected for Δ
13
C, KS, PH and TKW. Chromosomes 1A, 3A, 4A and 5A were the most important chromosomes, as they comprised quantitative trait loci (QTL) for Δ
13
C, a trait that can be used as an indirect tool for increased water-use efficiency in wheat. Environment-specific genomic regions were detected, indicating the presence of QTL-by-environment interaction. To produce suitable genotypes under contrasting water availability conditions, QTL × E interactions (and genotype-by-environment interaction) should be considered in the current spring wheat breeding program.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11032-015-0264-y</doi><tpages>12</tpages></addata></record> |
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subjects | Agronomy Bayesian analysis Biomedical and Life Sciences Biotechnology Carbon isotopes Chromosomes Clustering Computer simulation Crop yield Cultivars Gene mapping Gene sequencing Genetic distance Genomes Genomics Genotype-environment interactions Genotypes Genotyping Germplasm Kernels Life Sciences Linkage disequilibrium Mapping Markers Markov chains Molecular biology Phenotypic variations Plant biology Plant breeding Plant Genetics and Genomics Plant Pathology Plant Physiology Plant Sciences Quantitative trait loci Single-nucleotide polymorphism Spring wheat Water availability Water use Wheat |
title | Genome-wide association mapping of agronomic traits and carbon isotope discrimination in a worldwide germplasm collection of spring wheat using SNP markers |
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