Detection of rare functional variants using group ISIS
| dc.contributor.author | Niu, Yue | |
| dc.contributor.author | Hao, Ning | |
| dc.contributor.author | An, Lingling | |
| dc.date.accessioned | 2016-05-20T08:58:19Z | |
| dc.date.available | 2016-05-20T08:58:19Z | |
| dc.date.issued | 2011 | en |
| dc.identifier.citation | Niu et al. BMC Proceedings 2011, 5(Suppl 9):S108 http://www.biomedcentral.com/1753-6561/5/S9/S108 | en |
| dc.identifier.doi | 10.1186/1753-6561-5-S9-S108 | en |
| dc.identifier.uri | http://hdl.handle.net/10150/610089 | |
| dc.description.abstract | Genome-wide association studies have been firmly established in investigations of the associations between common genetic variants and complex traits or diseases. However, a large portion of complex traits and diseases cannot be explained well by common variants. Detecting rare functional variants becomes a trend and a necessity. Because rare variants have such a small minor allele frequency (e.g., <0.05), detecting functional rare variants is challenging. Group iterative sure independence screening (ISIS), a fast group selection tool, was developed to select important genes and the single-nucleotide polymorphisms within. The performance of the group ISIS and group penalization methods is compared for detecting important genes in the Genetic Analysis Workshop 17 data. The results suggest that the group ISIS is an efficient tool to discover genes and single-nucleotide polymorphisms associated to phenotypes. | |
| dc.language.iso | en | en |
| dc.publisher | BioMed Central | en |
| dc.relation.url | http://www.biomedcentral.com/1753-6561/5/S9/S108 | en |
| dc.rights | © 2011 Niu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0). | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/2.0/ | |
| dc.title | Detection of rare functional variants using group ISIS | en |
| dc.type | Article | en |
| dc.identifier.eissn | 1753-6561 | en |
| dc.contributor.department | Department of Mathematics, The University of Arizona, Tucson, AZ 85721, USA | en |
| dc.contributor.department | Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ 85721, USA | en |
| dc.identifier.journal | BMC Proceedings | en |
| dc.description.collectioninformation | This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu. | en |
| dc.eprint.version | Final published version | en |
| refterms.dateFOA | 2018-08-18T06:18:00Z | |
| html.description.abstract | Genome-wide association studies have been firmly established in investigations of the associations between common genetic variants and complex traits or diseases. However, a large portion of complex traits and diseases cannot be explained well by common variants. Detecting rare functional variants becomes a trend and a necessity. Because rare variants have such a small minor allele frequency (e.g., <0.05), detecting functional rare variants is challenging. Group iterative sure independence screening (ISIS), a fast group selection tool, was developed to select important genes and the single-nucleotide polymorphisms within. The performance of the group ISIS and group penalization methods is compared for detecting important genes in the Genetic Analysis Workshop 17 data. The results suggest that the group ISIS is an efficient tool to discover genes and single-nucleotide polymorphisms associated to phenotypes. |
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Except where otherwise noted, this item's license is described as © 2011 Niu et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0).