Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions
Abstract Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making predicti...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | The Plant Genome |
| Online Access: | https://doi.org/10.1002/tpg2.20553 |
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| _version_ | 1849340222860427264 |
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| author | Baber Ali Tristan Mary‐Huard Alain Charcosset Laurence Moreau Renaud Rincent |
| author_facet | Baber Ali Tristan Mary‐Huard Alain Charcosset Laurence Moreau Renaud Rincent |
| author_sort | Baber Ali |
| collection | DOAJ |
| description | Abstract Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization. It allows likely causal markers to account for a certain portion of genetic variance independently from the remaining markers. We systematically tested a list of 5110 GO terms for their predictive performance for physiological (platform traits) and productivity traits (field grain yield) in a maize (Zea mays L.) panel using genomic features best linear unbiased prediction (GFBLUP) model. Predictive abilities were compared to the classical genomic best linear unbiased prediction (GBLUP). Predictive gains with categorizing markers based on a given GO term strongly depend on the trait and on the growth conditions, as a term can be useful for a given trait in a given condition or somewhat similar conditions but not useful for the same trait in a different condition. Overall, results of all GFBLUP models compared to GBLUP show that the former might be less efficient than the latter. Even though we could not identify a prior criterion to determine which GO terms can offer benefit to a given trait, we could a posteriori find biological interpretations of the results, meaning that GFBLUP could be helpful if more about the gene functions and their relationships with the growth conditions was known. |
| format | Article |
| id | doaj-art-7e6b09b992d847e98c1f826627c99ef3 |
| institution | Kabale University |
| issn | 1940-3372 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Plant Genome |
| spelling | doaj-art-7e6b09b992d847e98c1f826627c99ef32025-08-20T03:43:57ZengWileyThe Plant Genome1940-33722025-03-01181n/an/a10.1002/tpg2.20553Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditionsBaber Ali0Tristan Mary‐Huard1Alain Charcosset2Laurence Moreau3Renaud Rincent4INRAE, CNRS, AgroParisTech, GQE–Le Moulon Université Paris‐Saclay Gif‐sur‐Yvette FranceINRAE, CNRS, AgroParisTech, GQE–Le Moulon Université Paris‐Saclay Gif‐sur‐Yvette FranceINRAE, CNRS, AgroParisTech, GQE–Le Moulon Université Paris‐Saclay Gif‐sur‐Yvette FranceINRAE, CNRS, AgroParisTech, GQE–Le Moulon Université Paris‐Saclay Gif‐sur‐Yvette FranceINRAE, CNRS, AgroParisTech, GQE–Le Moulon Université Paris‐Saclay Gif‐sur‐Yvette FranceAbstract Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization. It allows likely causal markers to account for a certain portion of genetic variance independently from the remaining markers. We systematically tested a list of 5110 GO terms for their predictive performance for physiological (platform traits) and productivity traits (field grain yield) in a maize (Zea mays L.) panel using genomic features best linear unbiased prediction (GFBLUP) model. Predictive abilities were compared to the classical genomic best linear unbiased prediction (GBLUP). Predictive gains with categorizing markers based on a given GO term strongly depend on the trait and on the growth conditions, as a term can be useful for a given trait in a given condition or somewhat similar conditions but not useful for the same trait in a different condition. Overall, results of all GFBLUP models compared to GBLUP show that the former might be less efficient than the latter. Even though we could not identify a prior criterion to determine which GO terms can offer benefit to a given trait, we could a posteriori find biological interpretations of the results, meaning that GFBLUP could be helpful if more about the gene functions and their relationships with the growth conditions was known.https://doi.org/10.1002/tpg2.20553 |
| spellingShingle | Baber Ali Tristan Mary‐Huard Alain Charcosset Laurence Moreau Renaud Rincent Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions The Plant Genome |
| title | Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions |
| title_full | Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions |
| title_fullStr | Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions |
| title_full_unstemmed | Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions |
| title_short | Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions |
| title_sort | improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions |
| url | https://doi.org/10.1002/tpg2.20553 |
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