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Characterization of functional variants in 33 blood pressure loci using 1000 genomes project data.

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dc.contributor.authorLim, JE-
dc.contributor.authorShin, YA-
dc.contributor.authorHong, KW-
dc.contributor.authorJin, HS-
dc.contributor.authorKoh, IS-
dc.contributor.authorOh, B-
dc.date.accessioned2014-06-02T05:44:47Z-
dc.date.available2014-06-02T05:44:47Z-
dc.date.issued2013-
dc.identifier.issn1976-9571-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/10351-
dc.description.abstractThrough 2011, GWASs have identified 33 genetic loci that are linked to blood pressure. Data from the 1000 Genomes Project were used to examine these loci. By searching nonsynonymous SNPs, promoter SNPs, splicing site SNPs, and gain- or loss-of-stop codon SNPs in 1000 Genomes Project data, we identified 2,113 functional variants in 66 genes in the 33 loci: 613 nonsynonymous SNPs, 1,425 promoter SNPs, 114 splice SNPs, and 15 gain- or loss-of-stop SNPs. There were no frameshift variations. Four hundred four of 613 nonsynonymous SNPs were predicted to be deleterious, based on 1000 Genomes Project data, and 1,114 of 1,425 promoter SNPs were predicted to influence the binding of transcription factors, using TFSearch. To determine whether these functional variants were causative factors of blood pressure, we analyzed KARE data, comprising 7,551 Korean individuals. The 24,962 SNPs in the 33 loci were imputed from the 1000 Genomes Project data into the KARE data. One hundred fourteen of 2,113 functional variants were successfully imputed and analyzed for their association with systolic blood pressure, diastolic blood pressure, and hypertension in the KARE cohort. As a result, 15 SNPs—3 nonsynonymous SNPs, 11 promoter SNPs, and 1 splice site SNP—showed association signals. These results, despite the low percentage of functional variants that were analyzed, provide valuable data on the candidate variants that govern blood pressure GWAS signals.-
dc.language.isoen-
dc.titleCharacterization of functional variants in 33 blood pressure loci using 1000 genomes project data.-
dc.typeArticle-
dc.contributor.affiliatedAuthor진, 현석-
dc.type.localJournal Papers-
dc.citation.titleGenes & genomics-
dc.citation.volume35-
dc.citation.number3-
dc.citation.date2013-
dc.citation.startPage387-
dc.citation.endPage393-
dc.identifier.bibliographicCitationGenes & genomics, 35(3). : 387-393, 2013-
dc.identifier.eissn2092-9293-
dc.relation.journalidJ019769571-
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Journal Papers > School of Medicine / Graduate School of Medicine > Medical Genetics
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