Different sets of QTLs influence fitness variation in yeast
Abstract Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in‐lab evolution (ILE) exp...
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| Format: | Article |
| Language: | English |
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Springer Nature
2010-02-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/msb.2010.1 |
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| author | Gal Hagit Romano Yonat Gurvich Ofer Lavi Igor Ulitsky Ron Shamir Martin Kupiec |
| author_facet | Gal Hagit Romano Yonat Gurvich Ofer Lavi Igor Ulitsky Ron Shamir Martin Kupiec |
| author_sort | Gal Hagit Romano |
| collection | DOAJ |
| description | Abstract Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in‐lab evolution (ILE) experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. The populations acquired different sets of affecting alleles, showing that evolution can provide alternative solutions to the same challenge. We measured the contribution of each allele to the phenotype. The sum of the effects of the QTLs was larger than the difference between the ancestor phenotype and the evolved strains, suggesting epistatic interactions between the QTLs. In parallel, a clinical isolated strain was used to map natural QTLs affecting growth at high pH. In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein‐interaction networks, candidate genes were defined and validated by gene disruption. Many of the QTLs found by both methods are not directly implied in pH homeostasis but have more general, and often regulatory, roles. |
| format | Article |
| id | doaj-art-4dc8446a7cb440a7a7f057318ef5b069 |
| institution | OA Journals |
| issn | 1744-4292 |
| language | English |
| publishDate | 2010-02-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-4dc8446a7cb440a7a7f057318ef5b0692025-08-20T02:18:25ZengSpringer NatureMolecular Systems Biology1744-42922010-02-016111110.1038/msb.2010.1Different sets of QTLs influence fitness variation in yeastGal Hagit Romano0Yonat Gurvich1Ofer Lavi2Igor Ulitsky3Ron Shamir4Martin Kupiec5Department of Molecular Microbiology and Biotechnology, Tel Aviv UniversityDepartment of Molecular Microbiology and Biotechnology, Tel Aviv UniversitySchool of Computer Sciences, Tel Aviv UniversitySchool of Computer Sciences, Tel Aviv UniversitySchool of Computer Sciences, Tel Aviv UniversityDepartment of Molecular Microbiology and Biotechnology, Tel Aviv UniversityAbstract Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in‐lab evolution (ILE) experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. The populations acquired different sets of affecting alleles, showing that evolution can provide alternative solutions to the same challenge. We measured the contribution of each allele to the phenotype. The sum of the effects of the QTLs was larger than the difference between the ancestor phenotype and the evolved strains, suggesting epistatic interactions between the QTLs. In parallel, a clinical isolated strain was used to map natural QTLs affecting growth at high pH. In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein‐interaction networks, candidate genes were defined and validated by gene disruption. Many of the QTLs found by both methods are not directly implied in pH homeostasis but have more general, and often regulatory, roles.https://doi.org/10.1038/msb.2010.1congenic linesgrowth on alkaliin‐lab evolutionQTL mappingSaccharomyces cerevisiae |
| spellingShingle | Gal Hagit Romano Yonat Gurvich Ofer Lavi Igor Ulitsky Ron Shamir Martin Kupiec Different sets of QTLs influence fitness variation in yeast Molecular Systems Biology congenic lines growth on alkali in‐lab evolution QTL mapping Saccharomyces cerevisiae |
| title | Different sets of QTLs influence fitness variation in yeast |
| title_full | Different sets of QTLs influence fitness variation in yeast |
| title_fullStr | Different sets of QTLs influence fitness variation in yeast |
| title_full_unstemmed | Different sets of QTLs influence fitness variation in yeast |
| title_short | Different sets of QTLs influence fitness variation in yeast |
| title_sort | different sets of qtls influence fitness variation in yeast |
| topic | congenic lines growth on alkali in‐lab evolution QTL mapping Saccharomyces cerevisiae |
| url | https://doi.org/10.1038/msb.2010.1 |
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