Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat
There has been a significant trend in the use of different statistical tools to analyse genotype × environment (GE) interaction for grain yield in multi‐environment trials. Several statistical models including 16 univariate stability methods and four multivariate models such as the additive main eff...
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| Veröffentlicht in: | Annals of applied biology 2021-05, Vol.178 (3), p.450-465 |
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| creator | Mohammadi, Reza Sadeghzadeh, Behzad Poursiahbidi, Mohammad Mehdi Ahmadi, Malak Masoud |
| description | There has been a significant trend in the use of different statistical tools to analyse genotype × environment (GE) interaction for grain yield in multi‐environment trials. Several statistical models including 16 univariate stability methods and four multivariate models such as the additive main effects and multiplicative interaction (AMMI), GGE biplot (G+GE biplot), and factorial regression and partial least squares regression were applied to investigate the GE interaction for grain‐yield data of 18 durum wheat genotypes grown in 14 environments (location‐year combinations). The main objectives were to use the different statistical models to evaluate GE interaction for grain yield in durum wheat and to investigate the effect of some climatic variables on the interactions. The main effect of environment, genotype and GE interactions were significant (p |
| doi_str_mv | 10.1111/aab.12648 |
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Several statistical models including 16 univariate stability methods and four multivariate models such as the additive main effects and multiplicative interaction (AMMI), GGE biplot (G+GE biplot), and factorial regression and partial least squares regression were applied to investigate the GE interaction for grain‐yield data of 18 durum wheat genotypes grown in 14 environments (location‐year combinations). The main objectives were to use the different statistical models to evaluate GE interaction for grain yield in durum wheat and to investigate the effect of some climatic variables on the interactions. The main effect of environment, genotype and GE interactions were significant (p < .01), and accounted for 85.1, 0.8 and 6.8% of total variation, respectively. Using the cluster and discriminant analyses, a pattern map developed simultaneously for clustering of stability methods and genotypes, which allowed identifying seven genotypic groups for genotypes and four groups for stability methods. The different stability groups explained genotypic performance differently, with or without respect to yield. The AMMI stability value, Wricke's ecovalence (Wi), Shukla's stability variance (σ2), Perkins and Jinks's (β and Dj) indices, joint regression parameters (bi and S2di, R2) and Tai's stability (σ and λ) methods did not correlate with genotypic mean yields, while the dynamic stability GGE distance and superiority measure (Pi) showed significant positive correlations with genotypic mean yields, showing selection based on these two methods would improve yield stability and performance. Using the applied methods, the breeding lines G12, G13 and G7 showed high mean yield and stability performance. The results also showed that the GE interactions were mostly influenced by the climatic data of rainfall, freezing days, minimum temperature and relative humidity. Although the multivariate methods provided valuable information on GE interaction, the univariate methods seem to be useful alternatives to complement improving screening efficiency.</description><identifier>ISSN: 0003-4746</identifier><identifier>EISSN: 1744-7348</identifier><identifier>DOI: 10.1111/aab.12648</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>AMMI ; Breeding ; Climate change ; Climate effects ; Climatic data ; Clustering ; Crop yield ; durum wheat ; Dynamic stability ; Environment models ; Environmental effects ; Freezing ; Genotype & phenotype ; Genotype-environment interactions ; Genotypes ; GGE biplot ; grain yield ; heat map ; Least squares method ; Mathematical models ; Mean ; Multivariate analysis ; partial least square regression ; Pattern analysis ; Rainfall ; Regression ; Relative humidity ; Stability analysis ; stability methods ; Statistical analysis ; Statistical models ; Triticum durum ; Wheat</subject><ispartof>Annals of applied biology, 2021-05, Vol.178 (3), p.450-465</ispartof><rights>2020 Association of Applied Biologists</rights><rights>2021 Association of Applied Biologists</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2128-feca7e4c3d8b6758d1ca52d4f3ac23e7144933dfa6c9a71d58eae9651e1c1a333</citedby><cites>FETCH-LOGICAL-c2128-feca7e4c3d8b6758d1ca52d4f3ac23e7144933dfa6c9a71d58eae9651e1c1a333</cites><orcidid>0000-0001-7694-0849</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Faab.12648$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Faab.12648$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Mohammadi, Reza</creatorcontrib><creatorcontrib>Sadeghzadeh, Behzad</creatorcontrib><creatorcontrib>Poursiahbidi, Mohammad Mehdi</creatorcontrib><creatorcontrib>Ahmadi, Malak Masoud</creatorcontrib><title>Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat</title><title>Annals of applied biology</title><description>There has been a significant trend in the use of different statistical tools to analyse genotype × environment (GE) interaction for grain yield in multi‐environment trials. Several statistical models including 16 univariate stability methods and four multivariate models such as the additive main effects and multiplicative interaction (AMMI), GGE biplot (G+GE biplot), and factorial regression and partial least squares regression were applied to investigate the GE interaction for grain‐yield data of 18 durum wheat genotypes grown in 14 environments (location‐year combinations). The main objectives were to use the different statistical models to evaluate GE interaction for grain yield in durum wheat and to investigate the effect of some climatic variables on the interactions. The main effect of environment, genotype and GE interactions were significant (p < .01), and accounted for 85.1, 0.8 and 6.8% of total variation, respectively. Using the cluster and discriminant analyses, a pattern map developed simultaneously for clustering of stability methods and genotypes, which allowed identifying seven genotypic groups for genotypes and four groups for stability methods. The different stability groups explained genotypic performance differently, with or without respect to yield. The AMMI stability value, Wricke's ecovalence (Wi), Shukla's stability variance (σ2), Perkins and Jinks's (β and Dj) indices, joint regression parameters (bi and S2di, R2) and Tai's stability (σ and λ) methods did not correlate with genotypic mean yields, while the dynamic stability GGE distance and superiority measure (Pi) showed significant positive correlations with genotypic mean yields, showing selection based on these two methods would improve yield stability and performance. Using the applied methods, the breeding lines G12, G13 and G7 showed high mean yield and stability performance. The results also showed that the GE interactions were mostly influenced by the climatic data of rainfall, freezing days, minimum temperature and relative humidity. Although the multivariate methods provided valuable information on GE interaction, the univariate methods seem to be useful alternatives to complement improving screening efficiency.</description><subject>AMMI</subject><subject>Breeding</subject><subject>Climate change</subject><subject>Climate effects</subject><subject>Climatic data</subject><subject>Clustering</subject><subject>Crop yield</subject><subject>durum wheat</subject><subject>Dynamic stability</subject><subject>Environment models</subject><subject>Environmental effects</subject><subject>Freezing</subject><subject>Genotype & phenotype</subject><subject>Genotype-environment interactions</subject><subject>Genotypes</subject><subject>GGE biplot</subject><subject>grain yield</subject><subject>heat map</subject><subject>Least squares method</subject><subject>Mathematical models</subject><subject>Mean</subject><subject>Multivariate analysis</subject><subject>partial least square regression</subject><subject>Pattern analysis</subject><subject>Rainfall</subject><subject>Regression</subject><subject>Relative humidity</subject><subject>Stability analysis</subject><subject>stability methods</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Triticum durum</subject><subject>Wheat</subject><issn>0003-4746</issn><issn>1744-7348</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kMlOwzAQhi0EEqVw4A0sceKQNo6d7VgqlkqVuMA5mtqT4Cpxiu20qsR78EC8GC5F3Jg5zKJvFv2EXLN4woJNAVYTlmSiOCEjlgsR5VwUp2QUxzGPRC6yc3Lh3DqUZVwmI_KxMB4bC16bhg5Gb8Fq8EjBKNoNrf9rOB8Y57WElna9wtZR31NtthiazYFo0PR-v0H69UnRbLXtTYfGB8ajBel1b0JO1WCHju7eEPwlOauhdXj1G8fk9eH-Zf4ULZ8fF_PZMpIJS4qoRgk5CslVscrytFBMQpooUXOQCcecCVFyrmrIZAk5U2mBgGWWMmSSAed8TG6Oeze2fx_Cw9W6H6wJJ6skZWUigrNA3R4paXvnLNbVxuoO7L5icXUQtwriVj_iBnZ6ZHe6xf3_YDWb3R0nvgEHdX9x</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Mohammadi, Reza</creator><creator>Sadeghzadeh, Behzad</creator><creator>Poursiahbidi, Mohammad Mehdi</creator><creator>Ahmadi, Malak Masoud</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0001-7694-0849</orcidid></search><sort><creationdate>202105</creationdate><title>Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat</title><author>Mohammadi, Reza ; Sadeghzadeh, Behzad ; Poursiahbidi, Mohammad Mehdi ; Ahmadi, Malak Masoud</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2128-feca7e4c3d8b6758d1ca52d4f3ac23e7144933dfa6c9a71d58eae9651e1c1a333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>AMMI</topic><topic>Breeding</topic><topic>Climate change</topic><topic>Climate effects</topic><topic>Climatic data</topic><topic>Clustering</topic><topic>Crop yield</topic><topic>durum wheat</topic><topic>Dynamic stability</topic><topic>Environment models</topic><topic>Environmental effects</topic><topic>Freezing</topic><topic>Genotype & phenotype</topic><topic>Genotype-environment interactions</topic><topic>Genotypes</topic><topic>GGE biplot</topic><topic>grain yield</topic><topic>heat map</topic><topic>Least squares method</topic><topic>Mathematical models</topic><topic>Mean</topic><topic>Multivariate analysis</topic><topic>partial least square regression</topic><topic>Pattern analysis</topic><topic>Rainfall</topic><topic>Regression</topic><topic>Relative humidity</topic><topic>Stability analysis</topic><topic>stability methods</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Triticum durum</topic><topic>Wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mohammadi, Reza</creatorcontrib><creatorcontrib>Sadeghzadeh, Behzad</creatorcontrib><creatorcontrib>Poursiahbidi, Mohammad Mehdi</creatorcontrib><creatorcontrib>Ahmadi, Malak Masoud</creatorcontrib><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Annals of applied biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mohammadi, Reza</au><au>Sadeghzadeh, Behzad</au><au>Poursiahbidi, Mohammad Mehdi</au><au>Ahmadi, Malak Masoud</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat</atitle><jtitle>Annals of applied biology</jtitle><date>2021-05</date><risdate>2021</risdate><volume>178</volume><issue>3</issue><spage>450</spage><epage>465</epage><pages>450-465</pages><issn>0003-4746</issn><eissn>1744-7348</eissn><abstract>There has been a significant trend in the use of different statistical tools to analyse genotype × environment (GE) interaction for grain yield in multi‐environment trials. Several statistical models including 16 univariate stability methods and four multivariate models such as the additive main effects and multiplicative interaction (AMMI), GGE biplot (G+GE biplot), and factorial regression and partial least squares regression were applied to investigate the GE interaction for grain‐yield data of 18 durum wheat genotypes grown in 14 environments (location‐year combinations). The main objectives were to use the different statistical models to evaluate GE interaction for grain yield in durum wheat and to investigate the effect of some climatic variables on the interactions. The main effect of environment, genotype and GE interactions were significant (p < .01), and accounted for 85.1, 0.8 and 6.8% of total variation, respectively. Using the cluster and discriminant analyses, a pattern map developed simultaneously for clustering of stability methods and genotypes, which allowed identifying seven genotypic groups for genotypes and four groups for stability methods. The different stability groups explained genotypic performance differently, with or without respect to yield. The AMMI stability value, Wricke's ecovalence (Wi), Shukla's stability variance (σ2), Perkins and Jinks's (β and Dj) indices, joint regression parameters (bi and S2di, R2) and Tai's stability (σ and λ) methods did not correlate with genotypic mean yields, while the dynamic stability GGE distance and superiority measure (Pi) showed significant positive correlations with genotypic mean yields, showing selection based on these two methods would improve yield stability and performance. Using the applied methods, the breeding lines G12, G13 and G7 showed high mean yield and stability performance. The results also showed that the GE interactions were mostly influenced by the climatic data of rainfall, freezing days, minimum temperature and relative humidity. Although the multivariate methods provided valuable information on GE interaction, the univariate methods seem to be useful alternatives to complement improving screening efficiency.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/aab.12648</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-7694-0849</orcidid></addata></record> |
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| subjects | AMMI Breeding Climate change Climate effects Climatic data Clustering Crop yield durum wheat Dynamic stability Environment models Environmental effects Freezing Genotype & phenotype Genotype-environment interactions Genotypes GGE biplot grain yield heat map Least squares method Mathematical models Mean Multivariate analysis partial least square regression Pattern analysis Rainfall Regression Relative humidity Stability analysis stability methods Statistical analysis Statistical models Triticum durum Wheat |
| title | Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat |
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