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Bhak, Jong
KOrean GenomIcs Center(KOGIC)
Research Interests
  • Geromics, genomics, bioinformatics, protein Engineering, OMICS


A public resource facilitating clinical use of genomes

DC Field Value Language Ball, Madeleine P. ko Thakuria, Joseph V. ko Zaranek, Alexander Wait ko Clegg, Tom ko Rosenbaum, Abraham M. ko Wu, Xiaodi ko Angrist, Misha ko Bhak, Jong Hwa ko Bobe, Jason ko Callow, Matthew J. ko Cano, Carlos ko Chou, Michael F. ko Chung, Wendy K. ko Douglas, Shawn M. ko Estep, Preston W. ko Gore, Athurva ko Hulick, Peter ko Labarga, Alberto ko Lee, Je-Hyuk ko Lunshof, Jeantine E. ko Kim, Byung Chul ko Kim, Jong-Il ko Li, Zhe ko Murray, Michael F. ko Nilsen, Geoffrey B. ko Peters, Brock A. ko Raman, Anugraha M. ko Rienhoff, Hugh Y. ko Robasky, Kimberly ko Wheeler, Matthew T. ko Vandewege, Ward ko Vorhaus, Daniel B. ko Yang, Joyce L. ko Yang, Luhan ko Aach, John ko Ashley, Euan A. ko Drmanac, Radoje ko Kim, Seong-Jin ko Li, Jin Billy ko Peshkin, Leonid ko Seidman, Christine E. ko Seo, Jeong-Sun ko Zhang, Kun ko Rehm, Heidi L. ko Church, George M. ko 2014-12-26T00:13:32Z - 2014-12-24 ko 2012-07 -
dc.identifier.citation PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, v.109, no.30, pp.11920 - 11927 ko
dc.identifier.issn 0027-8424 ko
dc.identifier.uri -
dc.identifier.uri ko
dc.description.abstract Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review boardapproved "open consent" process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10).We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain - we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research. ko
dc.description.statementofresponsibility close -
dc.language ENG ko
dc.publisher NATL ACAD SCIENCES ko
dc.subject Genome interpretation ko
dc.subject Genomic medicine ko
dc.subject Human genetics ko
dc.title A public resource facilitating clinical use of genomes ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-84864341757 ko
dc.identifier.wosid 000306992700018 ko
dc.type.rims ART ko
dc.description.wostc 55 *
dc.description.scopustc 58 * 2015-05-06 * 2014-12-24 *
dc.identifier.doi 10.1073/pnas.1201904109 ko
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