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

机译:使用1000个基因组数据表征33个血压基因座中的功能变异

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摘要

Through 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.
机译:到2011年,GWAS已经确定了33个与血压相关的遗传基因座。来自1000个基因组计划的数据用于检查这些基因座。通过在1000个基因组计划数据中搜索非同义SNP,启动子SNP,剪接位点SNP和终止或终止密码子SNP,我们在33个基因座的66个基因中鉴定了2,113个功能变体:613个非同义SNP,1,425个启动子SNP, 114个拼接SNP,以及15个增益停止丢失SNP。没有移码变化。根据1000个基因组计划数据,预测613个非同义SNP中的404个有害,使用TFSearch,预测1,425个启动子SNP中的1,114个会影响转录因子的结合。为了确定这些功能变异是否是血压的致病因素,我们分析了KARE数据,其中包括7,551名韩国人。将33个基因座中的24,962个SNP从1000个基因组计划数据推算到KARE数据中。成功估算了2113个功能变体中的14个,并分析了它们与KARE队列中的收缩压,舒张压和高血压的关系。结果,15个SNP(3个非同义SNP,11个启动子SNP和1个剪接位点SNP)显示出关联信号。尽管分析的功能变体的百分比较低,但这些结果仍可提供有关控制血压GWAS信号的候选变体的有价值的数据。

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