Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77493
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dc.contributorDepartment of Applied Biology and Chemical Technology-
dc.contributorDepartment of Applied Mathematics-
dc.creatorTsui, NBY-
dc.creatorCheng, G-
dc.creatorChung, T-
dc.creatorLam, CWK-
dc.creatorYee, A-
dc.creatorChung, PKC-
dc.creatorKwan, TK-
dc.creatorKo, E-
dc.creatorHe, D-
dc.creatorWong, WT-
dc.creatorLau, JYN-
dc.creatorLau, LT-
dc.creatorFok, M-
dc.date.accessioned2018-08-28T01:32:44Z-
dc.date.available2018-08-28T01:32:44Z-
dc.identifier.urihttp://hdl.handle.net/10397/77493-
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rights© The Author(s) 2018en_US
dc.rightsThe following publication Tsui, N.B.Y., Cheng, G., Chung, T. et al. Population-Wide Genetic Risk Prediction of Complex Diseases: A Pilot Feasibility Study in Macau Population for Precision Public Healthcare Planning. Sci Rep 8, 1853 (2018) is available at https://dx.doi.org/10.1038/s41598-017-19017-yen_US
dc.titlePopulation-wide genetic risk prediction of complex diseases : a pilot feasibility study in macau population for precision public healthcare planningen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume8-
dc.identifier.issue1-
dc.identifier.doi10.1038/s41598-017-19017-y-
dcterms.abstractThe genetic bases of many common diseases have been identified through genome-wide association studies in the past decade. However, the application of this approach on public healthcare planning has not been well established. Using Macau with population of around 650,000 as a basis, we conducted a pilot study to evaluate the feasibility of population genomic research and its potential on public health decisions. By performing genome-wide SNP genotyping of over a thousand Macau individuals, we evaluated the population genetic risk profiles of 47 non-communicable diseases and traits, as well as two traits associated with influenza infection. We found that for most of the diseases, the genetic risks of Macau population were different from those of Caucasian, but with similar profile with mainland Chinese. We also identified a panel of diseases that Macau population may have a high or elevated genetic risks. This pilot study showed that (1) population genomic study is feasible in Asian regions like Macau (2) Macau may have different profile of population-based genetic risks than Caucasians, (3) the different prevalence of genetic risk profile indicates the importance of Asian-specific studies for Asian populations and (4) the results generated may have an impact for going forward healthcare planning.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationScientific reports, 30 2018, v. 8, no. 1, 1853, p. 1-11-
dcterms.isPartOfScientific reports-
dcterms.issued2018-
dc.identifier.isiWOS:000423508900024-
dc.identifier.scopus2-s2.0-85041342225-
dc.identifier.eissn2045-2322-
dc.identifier.artn1853-
dc.identifier.rosgroupid2017002178-
dc.description.ros2017-2018 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201808 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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