[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126728-en":3,"doc-seo-126728-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126728,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Relating enhancer genetic variation across mammals to complex phenotypes using machine learning","Enhancer genetic variation across multiple mammalian species is analyzed to understand how regulatory changes contribute to complex phenotypes. The study integrates publicly available epigenomic and genomic datasets, including ATAC-seq, Hi-C, and reference genomes, and applies machine learning models within a phenotype association framework. Results link enhancer variation to phenotypic traits across mammals, supported by motif discovery and model predictions. Supplementary materials provide additional figures, tables, and reproducibility information for the associated computational pipeline.","UC Santa Cruz  \nUC Santa Cruz Previously Published Works  \nTitle  \nRelating enhancer genetic variation across mammals to complex phenotypes using machine learning  \nPermalink  \n[https://escholarship.org/uc/item/4pt562cc](https://escholarship.org/uc/item/4pt562cc)  \nJournal  \nScience, 380(6643)  \nISSN  \n0036-8075  \nAuthors  \nKaplow, Irene M  \nLawler, Alyssa J Schäffer, Daniel Eet al.  \nPublication Date  \n2023-04-28  \nDOI  \n10.1126/science.abm7993  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAuthor Manuscr ipt Author Manuscr ipt Author Manuscr ipt Author Manuscript  \n\n|  | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Science. Author manuscript; available in PMC 2023 July 05. |\n| --- | --- |\n\nPublished in final edited form as:  \nScience. 2023 April 28; 380(6643): eabm7993 . doi:10.1126/science.abm7993 .  \nRelating enhancer genetic variation across mammals to complex phenotypes using machine learning  \nIrene M. Kaplow 1,2,*,†, Alyssa J. Lawler2,3,†,‡, Daniel E. Schäffer1,†, Chaitanya Srinivasan 1 , Heather H. Sestili 1 , Morgan E. Wirthlin 1,2,§ , BaDoi N. Phan 1,2,4 , Kavya Prasad 1,¶ , Ashley R. Brown 1 , Xiaomeng Zhang 1,¶ , Kathleen Foley5,\\# , Diane P. Genereux6,7 ,  \n**  \nZoonomia Consortium  \n,  \nElinor K. Karlsson6,7 , Kerstin Lindblad-Toh6,8 , Wynn K. Meyer5 , Andreas R. Pfenning 1,2,3,*  \n1 Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA.  \n2 Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.  \n3 Department of Biology, Carnegie Mellon University, Pittsburgh, PA, USA.  \n*C†Torhessepandthngrsacthorntrib.iukaptedoqw@cuallyst.comthuis.edwr(Ik..M.K.); [apfenning@cmu.edu](apfenning@cmu.edu) (A.R.P.).  \n‡Present address: Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA.  \n§Present address: Allen Institute for Brain Science, Seattle, WA, USA.  \n¶Present address: Cancer Program, Broad Institute, Cambridge, MA, USA.  \n\\#Present address: College of Law, University of Iowa, Iowa City, IA, USA.  \n**Zoonomia Consortium collaborators and affiliations are listed at the end of this paper.  \nAuthor contributions: I.M.K., A.J.L., and D.E.S. are listed as co-first authors in last name–alphabetical order because they contributed equally to the manuscript. Conceptualization: I.M.K. and A.R.P. Data curation: I.M.K., C.S., B.N.P., A.J.L., W.K.M., K.F., and D.P.G. Formal analysis: I.M.K., D.E.S., A.J.L., C.S., H.H.S., and B.N.P. Funding acquisition: A.R.P., A.J.L., B.N.P., E.K.K., D.P.G., and K.L.-T. Investigation: I.M.K., A.J.L., D.E.S., C.S., M.E.W., H.H.S., B.N.P., K.P., A.R.B., and A.R.P. Methodology development: I.M.K., A.J.L., D.E.S., C.S., and A.R.P. Supervision: I.M.K., A.R.P., A.J.L., M.E.W., E.K.K., and K.L.-T. Software implementation: D.E.S., I.M.K., A.J.L., C.S., H.H.S., M.E.W., W.K.M., X.Z., and K.F. Visualization: I.M.K., D.E.S., C.S., A.J.L., H.H.S., and A.R.P. Manuscript preparation: I.M.K., D.E.S., A.J.L., A.R.P., C.S., and H.H.S. Manuscript review and editing: All authors.  \nCompeting interests: E.K.K. is on the advisory board of Fauna Bio. All other authors declare that they have no competing interests. Diversity and inclusion: One or more of the authors of this paper self-identifies as a member of the LGBTQ+ community.  \nData and materials availability: Publicly available ATAC-seq data were obtained from Gene Expression Omnibus accessions GSE161374, GSE146897, GSE137311, and GSE159815; China National GeneBank accession CNP0000198; and ArrayExpress accession E-MTAB-2633. Unpublished ATAC-seq data generated by the Pfenning lab can be found under accession GSE187366. The tree used for the phenotype association pipeline can be obtained in (68). Publicly available genomesand annotations were downloaded from NCBI Assembly and the UCSC Genome Browser. Publicly available human Hi-C data were accessed at [http://hugin2.genetics.unc.edu/Project/hugin/](http://hugin2.genetic","cbCaioUzlHoeX16N","https://ap.wps.com/l/cbCaioUzlHoeX16N","pdf",2191167,1,37,"English","en",105,"# Materials and Methods\n## Supplementary Text\n## Supplementary Figures\n## Supplementary Tables\n## References\n## Data and Materials Availability","[{\"question\":\"What is the main research focus of the document?\",\"answer\":\"It focuses on linking enhancer genetic variation across mammals to complex phenotypes using machine learning models and a phenotype association pipeline.\"},{\"question\":\"Which types of biological data are referenced for the analysis?\",\"answer\":\"The document cites publicly available ATAC-seq and Hi-C data, along with genome and annotation resources, and mentions accession identifiers for those datasets.\"},{\"question\":\"Where can supplementary information and model resources be found?\",\"answer\":\"Supplementary materials list 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