Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments

As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Genetics (Austin) 1991, Vol.127 (1), p.181-197
Hauptverfasser: Paterson, A.H, Damon, S, Hewitt, J.D, Zamir, D, Rabinowitch, H.D, Lincoln, S.E, Lander, E.S, Tanksley, S.D
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 197
container_issue 1
container_start_page 181
container_title Genetics (Austin)
container_volume 127
creator Paterson, A.H
Damon, S
Hewitt, J.D
Zamir, D
Rabinowitch, H.D
Lincoln, S.E
Lander, E.S
Tanksley, S.D
description As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 only in a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous
doi_str_mv 10.1093/genetics/127.1.181
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1204303</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>80518958</sourcerecordid><originalsourceid>FETCH-LOGICAL-c579t-f126ce485f63dcf80844d279dc50a7af9d51d304121535173061bd48422bbe8f3</originalsourceid><addsrcrecordid>eNqFUU1v1DAQjRCobAt_AAnhCz2RrceOE6cHJFTxJRVxgJ6tWcfJGiX21nZ21X-Pt7vQcuJked6bN2_mFcUroEugLb8YjDPJ6ngBrFnCEiQ8KRbQVrxkNYenxYJSqMu64fC8OI3xF6W0boU8KU5gX6T1okjfjOvMaNGRHnXyIZI5F8J4Z91Abmd0ySZMdmtICmhTJNaR5CdM_pJoP20w2OgdQR18jCRujLYmviN7ayH3eZc_6Dpi3NYG7ybjUnxRPOtxjObl8T0rbj59_Hn1pbz-_vnr1YfrUoumTWUPrNamkqKvead7SWVVdaxpOy0oNti3nYCO0woYCC6g4bSGVVfJirHVysienxXvD7qbeTWZTufZAUe1CXbCcKc8WvUv4uxaDX6rgNGKU54Fzo8Cwd_OJiY12ajNOKIzfo5KUgEyX_S_RBBty2hDM5EdiPf3Cqb_6wao2oeq_oSaTTQKVA41N71-vMdDyyHFjL894hg1jn1Ap218oLWCyfqed3S5tsN6Z4NRccJxzKqgdrvd44FvDsQevcIhR6xufjAKnLIGoGXAfwP61MZt</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>15992070</pqid></control><display><type>article</type><title>Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments</title><source>MEDLINE</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Paterson, A.H ; Damon, S ; Hewitt, J.D ; Zamir, D ; Rabinowitch, H.D ; Lincoln, S.E ; Lander, E.S ; Tanksley, S.D</creator><creatorcontrib>Paterson, A.H ; Damon, S ; Hewitt, J.D ; Zamir, D ; Rabinowitch, H.D ; Lincoln, S.E ; Lander, E.S ; Tanksley, S.D</creatorcontrib><description>As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 only in a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii). One possible explanation of this similarity is that genetic factors at some of the same loci may affect the traits in the two distantly-related wild species. Potentially major implications of such similarity across broad genetic distances are discussed, in regard to plant and animal breeding, germplasm introgression, and cloning of QTLs.</description><identifier>ISSN: 0016-6731</identifier><identifier>ISSN: 1943-2631</identifier><identifier>EISSN: 1943-2631</identifier><identifier>DOI: 10.1093/genetics/127.1.181</identifier><identifier>PMID: 1673106</identifier><identifier>CODEN: GENTAE</identifier><language>eng</language><publisher>Bethesda, MD: Genetics Soc America</publisher><subject>Agronomy. Soil science and plant productions ; Biological and medical sciences ; Biological Evolution ; Breeding ; Chromosome Mapping ; Classical genetics, quantitative genetics, hybrids ; Crosses, Genetic ; crossing ; Environment ; Fruit - genetics ; Fruit - metabolism ; fruits ; Fundamental and applied biological sciences. Psychology ; gene dosage ; gene interaction ; Genetic Markers - genetics ; genetic variation ; Genetic Variation - genetics ; Genetics of eukaryotes. Biological and molecular evolution ; Genotype ; genotype-environment interaction ; heritability ; Hydrogen-Ion Concentration ; inheritance (genetics) ; Investigations ; loci ; pedigree ; Phenotype ; Plants - genetics ; Polymorphism, Restriction Fragment Length ; prediction ; Pteridophyta, spermatophyta ; quantitative trait loci mapping ; quantitative traits ; Recombination, Genetic ; size ; Solanum cheesmaniae ; Solanum lycopersicum var. lycopersicum ; solubility ; soluble solids ; Species Specificity ; Vegetals</subject><ispartof>Genetics (Austin), 1991, Vol.127 (1), p.181-197</ispartof><rights>1991 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c579t-f126ce485f63dcf80844d279dc50a7af9d51d304121535173061bd48422bbe8f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,781,785,886,4025,27928,27929,27930</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=19528606$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/1673106$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Paterson, A.H</creatorcontrib><creatorcontrib>Damon, S</creatorcontrib><creatorcontrib>Hewitt, J.D</creatorcontrib><creatorcontrib>Zamir, D</creatorcontrib><creatorcontrib>Rabinowitch, H.D</creatorcontrib><creatorcontrib>Lincoln, S.E</creatorcontrib><creatorcontrib>Lander, E.S</creatorcontrib><creatorcontrib>Tanksley, S.D</creatorcontrib><title>Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments</title><title>Genetics (Austin)</title><addtitle>Genetics</addtitle><description>As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 only in a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii). One possible explanation of this similarity is that genetic factors at some of the same loci may affect the traits in the two distantly-related wild species. Potentially major implications of such similarity across broad genetic distances are discussed, in regard to plant and animal breeding, germplasm introgression, and cloning of QTLs.</description><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>Biological Evolution</subject><subject>Breeding</subject><subject>Chromosome Mapping</subject><subject>Classical genetics, quantitative genetics, hybrids</subject><subject>Crosses, Genetic</subject><subject>crossing</subject><subject>Environment</subject><subject>Fruit - genetics</subject><subject>Fruit - metabolism</subject><subject>fruits</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>gene dosage</subject><subject>gene interaction</subject><subject>Genetic Markers - genetics</subject><subject>genetic variation</subject><subject>Genetic Variation - genetics</subject><subject>Genetics of eukaryotes. Biological and molecular evolution</subject><subject>Genotype</subject><subject>genotype-environment interaction</subject><subject>heritability</subject><subject>Hydrogen-Ion Concentration</subject><subject>inheritance (genetics)</subject><subject>Investigations</subject><subject>loci</subject><subject>pedigree</subject><subject>Phenotype</subject><subject>Plants - genetics</subject><subject>Polymorphism, Restriction Fragment Length</subject><subject>prediction</subject><subject>Pteridophyta, spermatophyta</subject><subject>quantitative trait loci mapping</subject><subject>quantitative traits</subject><subject>Recombination, Genetic</subject><subject>size</subject><subject>Solanum cheesmaniae</subject><subject>Solanum lycopersicum var. lycopersicum</subject><subject>solubility</subject><subject>soluble solids</subject><subject>Species Specificity</subject><subject>Vegetals</subject><issn>0016-6731</issn><issn>1943-2631</issn><issn>1943-2631</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1991</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFUU1v1DAQjRCobAt_AAnhCz2RrceOE6cHJFTxJRVxgJ6tWcfJGiX21nZ21X-Pt7vQcuJked6bN2_mFcUroEugLb8YjDPJ6ngBrFnCEiQ8KRbQVrxkNYenxYJSqMu64fC8OI3xF6W0boU8KU5gX6T1okjfjOvMaNGRHnXyIZI5F8J4Z91Abmd0ySZMdmtICmhTJNaR5CdM_pJoP20w2OgdQR18jCRujLYmviN7ayH3eZc_6Dpi3NYG7ybjUnxRPOtxjObl8T0rbj59_Hn1pbz-_vnr1YfrUoumTWUPrNamkqKvead7SWVVdaxpOy0oNti3nYCO0woYCC6g4bSGVVfJirHVysienxXvD7qbeTWZTufZAUe1CXbCcKc8WvUv4uxaDX6rgNGKU54Fzo8Cwd_OJiY12ajNOKIzfo5KUgEyX_S_RBBty2hDM5EdiPf3Cqb_6wao2oeq_oSaTTQKVA41N71-vMdDyyHFjL894hg1jn1Ap218oLWCyfqed3S5tsN6Z4NRccJxzKqgdrvd44FvDsQevcIhR6xufjAKnLIGoGXAfwP61MZt</recordid><startdate>1991</startdate><enddate>1991</enddate><creator>Paterson, A.H</creator><creator>Damon, S</creator><creator>Hewitt, J.D</creator><creator>Zamir, D</creator><creator>Rabinowitch, H.D</creator><creator>Lincoln, S.E</creator><creator>Lander, E.S</creator><creator>Tanksley, S.D</creator><general>Genetics Soc America</general><general>Genetics Society of America</general><scope>FBQ</scope><scope>IQODW</scope><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>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>1991</creationdate><title>Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments</title><author>Paterson, A.H ; Damon, S ; Hewitt, J.D ; Zamir, D ; Rabinowitch, H.D ; Lincoln, S.E ; Lander, E.S ; Tanksley, S.D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c579t-f126ce485f63dcf80844d279dc50a7af9d51d304121535173061bd48422bbe8f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1991</creationdate><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>Biological Evolution</topic><topic>Breeding</topic><topic>Chromosome Mapping</topic><topic>Classical genetics, quantitative genetics, hybrids</topic><topic>Crosses, Genetic</topic><topic>crossing</topic><topic>Environment</topic><topic>Fruit - genetics</topic><topic>Fruit - metabolism</topic><topic>fruits</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>gene dosage</topic><topic>gene interaction</topic><topic>Genetic Markers - genetics</topic><topic>genetic variation</topic><topic>Genetic Variation - genetics</topic><topic>Genetics of eukaryotes. Biological and molecular evolution</topic><topic>Genotype</topic><topic>genotype-environment interaction</topic><topic>heritability</topic><topic>Hydrogen-Ion Concentration</topic><topic>inheritance (genetics)</topic><topic>Investigations</topic><topic>loci</topic><topic>pedigree</topic><topic>Phenotype</topic><topic>Plants - genetics</topic><topic>Polymorphism, Restriction Fragment Length</topic><topic>prediction</topic><topic>Pteridophyta, spermatophyta</topic><topic>quantitative trait loci mapping</topic><topic>quantitative traits</topic><topic>Recombination, Genetic</topic><topic>size</topic><topic>Solanum cheesmaniae</topic><topic>Solanum lycopersicum var. lycopersicum</topic><topic>solubility</topic><topic>soluble solids</topic><topic>Species Specificity</topic><topic>Vegetals</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Paterson, A.H</creatorcontrib><creatorcontrib>Damon, S</creatorcontrib><creatorcontrib>Hewitt, J.D</creatorcontrib><creatorcontrib>Zamir, D</creatorcontrib><creatorcontrib>Rabinowitch, H.D</creatorcontrib><creatorcontrib>Lincoln, S.E</creatorcontrib><creatorcontrib>Lander, E.S</creatorcontrib><creatorcontrib>Tanksley, S.D</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Genetics (Austin)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Paterson, A.H</au><au>Damon, S</au><au>Hewitt, J.D</au><au>Zamir, D</au><au>Rabinowitch, H.D</au><au>Lincoln, S.E</au><au>Lander, E.S</au><au>Tanksley, S.D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments</atitle><jtitle>Genetics (Austin)</jtitle><addtitle>Genetics</addtitle><date>1991</date><risdate>1991</risdate><volume>127</volume><issue>1</issue><spage>181</spage><epage>197</epage><pages>181-197</pages><issn>0016-6731</issn><issn>1943-2631</issn><eissn>1943-2631</eissn><coden>GENTAE</coden><abstract>As part of ongoing studies regarding the genetic basis of quantitative variation in phenotype, we have determined the chromosomal locations of quantitative trait loci (QTLs) affecting fruit size, soluble solids concentration, and pH, in a cross between the domestic tomato (Lycopersicon esculentum Mill.) and a closely-related wild species, L. cheesmanii. Using a RFLP map of the tomato genome, we compared the inheritance patterns of polymorphisms in 350 F2 individuals with phenotypes scored in three different ways: (1) from the F2 progeny themselves, grown near Davis, California; (2) from F3 families obtained by selfing each F2 individual, grown near Gilroy, California (F3-CA); and (3) from equivalent F3 families grown near Rehovot, Israel (F3-IS). Maximum likelihood methods were used to estimate the approximate chromosomal locations, phenotypic effects (both additive effects and dominance deviations), and gene action of QTLs underlying phenotypic variation in each of these three environments. A total of 29 putative QTLs were detected in the three environments. These QTLs were distributed over 11 of the 12 chromosomes, accounted for 4.7-42.0% of the phenotypic variance in a trait, and showed different types of gene action. Among these 29 QTLs, 4 were detected in all three environments, 10 in two environments, and 15 only in a single environment. The two California environments were most similar, sharing 11/25 (44%) QTLs, while the Israel environment was quite different, sharing 7/20 (35%) and 5/26 (19%) QTLs with the respective California environments. One major goal of QTL mapping is to predict, with maximum accuracy, which individuals will produce progeny showing particular phenotypes. Traditionally, the phenotype of an individual alone has been used to predict the phenotype of its progeny. Our results suggested that, for a trait with low heritability (soluble solids), the phenotype of F3 progeny could be predicted more accurately from the genotype of the F2 parent at QTLs than from the phenotype of the F2 individual. For a trait with intermediate heritability (fruit pH), QTL genotype and observed phenotype were about equally effective at predicting progeny phenotype. For a trait with high heritability (mass per fruit), knowing the QTL genotype of an individual added little if any predictive value, to simply knowing the phenotype. The QTLs mapped in the L. esculentum X L. cheesmanii F2 appear to be at similar locations to many of those mapped in a previous cross with a different wild tomato (L. chmielewskii). One possible explanation of this similarity is that genetic factors at some of the same loci may affect the traits in the two distantly-related wild species. Potentially major implications of such similarity across broad genetic distances are discussed, in regard to plant and animal breeding, germplasm introgression, and cloning of QTLs.</abstract><cop>Bethesda, MD</cop><pub>Genetics Soc America</pub><pmid>1673106</pmid><doi>10.1093/genetics/127.1.181</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0016-6731
ispartof Genetics (Austin), 1991, Vol.127 (1), p.181-197
issn 0016-6731
1943-2631
1943-2631
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1204303
source MEDLINE; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection
subjects Agronomy. Soil science and plant productions
Biological and medical sciences
Biological Evolution
Breeding
Chromosome Mapping
Classical genetics, quantitative genetics, hybrids
Crosses, Genetic
crossing
Environment
Fruit - genetics
Fruit - metabolism
fruits
Fundamental and applied biological sciences. Psychology
gene dosage
gene interaction
Genetic Markers - genetics
genetic variation
Genetic Variation - genetics
Genetics of eukaryotes. Biological and molecular evolution
Genotype
genotype-environment interaction
heritability
Hydrogen-Ion Concentration
inheritance (genetics)
Investigations
loci
pedigree
Phenotype
Plants - genetics
Polymorphism, Restriction Fragment Length
prediction
Pteridophyta, spermatophyta
quantitative trait loci mapping
quantitative traits
Recombination, Genetic
size
Solanum cheesmaniae
Solanum lycopersicum var. lycopersicum
solubility
soluble solids
Species Specificity
Vegetals
title Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-12T00%3A14%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Mendelian%20factors%20underlying%20quantitative%20traits%20in%20tomato:%20comparison%20across%20species,%20generations,%20and%20environments&rft.jtitle=Genetics%20(Austin)&rft.au=Paterson,%20A.H&rft.date=1991&rft.volume=127&rft.issue=1&rft.spage=181&rft.epage=197&rft.pages=181-197&rft.issn=0016-6731&rft.eissn=1943-2631&rft.coden=GENTAE&rft_id=info:doi/10.1093/genetics/127.1.181&rft_dat=%3Cproquest_pubme%3E80518958%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=15992070&rft_id=info:pmid/1673106&rfr_iscdi=true