Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
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- dc.contributor.author Kaplow, Irene M.
- dc.contributor.author Lawler, Alyssa J.
- dc.contributor.author Schäffer, Daniel E.
- dc.contributor.author Srinivasan, Chaitanya
- dc.contributor.author Sestili, Heather H.
- dc.contributor.author Wirthlin, Morgan E.
- dc.contributor.author Phan, BaDoi N.
- dc.contributor.author Prasad, Kavya
- dc.contributor.author Brown, Ashley
- dc.contributor.author Zhang, Xiaomeng
- dc.contributor.author Foley, Kathleen
- dc.contributor.author Genereux, Diane P.
- dc.contributor.author Zoonomia Consortium
- dc.contributor.author Karlsson, Elinor K.
- dc.contributor.author Lindblad-Toh, Kerstin
- dc.contributor.author Meyer, Wynn K.
- dc.contributor.author Pfenning, Andreas R.
- dc.date.accessioned 2024-03-25T07:10:14Z
- dc.date.available 2024-03-25T07:10:14Z
- dc.date.issued 2023
- dc.description.abstract Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challenging because enhancer activity can be tissue-dependent and functionally conserved despite low sequence conservation. We developed the Tissue-Aware Conservation Inference Toolkit (TACIT) to associate candidate enhancers with species' phenotypes using predictions from machine learning models trained on specific tissues. Applying TACIT to associate motor cortex and parvalbumin-positive interneuron enhancers with neurological phenotypes revealed dozens of enhancer-phenotype associations, including brain size-associated enhancers that interact with genes implicated in microcephaly or macrocephaly. TACIT provides a foundation for identifying enhancers associated with the evolution of any convergently evolved phenotype in any large group of species with aligned genomes.
- dc.format.mimetype application/pdf
- dc.identifier.citation Kaplow IM, Lawler AJ, Schäffer DE, Srinivasan C, Sestili HH, Wirthlin ME, et al. Relating enhancer genetic variation across mammals to complex phenotypes using machine learning. Science. 2023 Apr 28;380(6643):eabm7993. DOI: 10.1126/science.abm7993
- dc.identifier.doi http://dx.doi.org/10.1126/science.abm7993
- dc.identifier.issn 0036-8075
- dc.identifier.uri http://hdl.handle.net/10230/59552
- dc.language.iso eng
- dc.publisher American Association for the Advancement of Science (AAAS)
- dc.relation.ispartof Science. 2023 Apr 28;380(6643):eabm7993
- dc.rights This is the author’s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science. 2023 Apr 28;380(6643):eabm7993, DOI: 10.1126/science.abm7993.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.other Genètica animal
- dc.title Relating enhancer genetic variation across mammals to complex phenotypes using machine learning
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/acceptedVersion