metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.
Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental str...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1011553 |
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| _version_ | 1832540331431690240 |
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| author | Annaïg De Walsche Alexis Vergne Renaud Rincent Fabrice Roux Stéphane Nicolas Claude Welcker Sofiane Mezmouk Alain Charcosset Tristan Mary-Huard |
| author_facet | Annaïg De Walsche Alexis Vergne Renaud Rincent Fabrice Roux Stéphane Nicolas Claude Welcker Sofiane Mezmouk Alain Charcosset Tristan Mary-Huard |
| author_sort | Annaïg De Walsche |
| collection | DOAJ |
| description | Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package. |
| format | Article |
| id | doaj-art-821de40241594a058146a76dd9000d5a |
| institution | Kabale University |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj-art-821de40241594a058146a76dd9000d5a2025-02-05T05:31:00ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042025-01-01211e101155310.1371/journal.pgen.1011553metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.Annaïg De WalscheAlexis VergneRenaud RincentFabrice RouxStéphane NicolasClaude WelckerSofiane MezmoukAlain CharcossetTristan Mary-HuardElucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package.https://doi.org/10.1371/journal.pgen.1011553 |
| spellingShingle | Annaïg De Walsche Alexis Vergne Renaud Rincent Fabrice Roux Stéphane Nicolas Claude Welcker Sofiane Mezmouk Alain Charcosset Tristan Mary-Huard metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. PLoS Genetics |
| title | metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. |
| title_full | metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. |
| title_fullStr | metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. |
| title_full_unstemmed | metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. |
| title_short | metaGE: Investigating genotype x environment interactions through GWAS meta-analysis. |
| title_sort | metage investigating genotype x environment interactions through gwas meta analysis |
| url | https://doi.org/10.1371/journal.pgen.1011553 |
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