Evidence map›Paper›PMID 25820152›Full record

ArticleG3 (Bethesda, Md.)2015

Novel Genes Affecting Blood Pressure Detected Via Gene-Based Association Analysis.

Huan Zhang, Xing-Bo Mo, Tan Xu, Xiao-Qing Bu, Shu-Feng Lei, Yong-Hong Zhang

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Huan ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China.
Xing-Bo MoJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
Tan XuJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China.
Xiao-Qing BuJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China.
Shu-Feng LeiJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
Yong-Hong ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu, People's Republic of China yhzhang@suda.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertension is a common disorder and one of the most important risk factors for cardiovascular diseases. The aim of this study was to identify more novel genes for blood pressure. Based on the publically available SNP-based P values of a meta-analysis of genome-wide association studies, we performed an initial gene-based association study in a total of 69,395 individuals. To find supplementary evidence to support the importance of the identified genes, we performed GRAIL (gene relationships among implicated loci) analysis, protein-protein interaction analysis, functional annotation clustering analysis, coronary artery disease association analysis, and other bioinformatics analyses. Approximately 22,129 genes on the human genome were analyzed for blood pressure in gene-based association analysis. A total of 43 genes were statistically significant after Bonferroni correction (P < 2.3×10(-6)). The evidence obtained from the analyses of this study suggested the importance of ID1 (P = 2.0×10(-6)), CYP17A1 (P = 4.58×10(-9)), ATXN2 (P = 1.07×10(-13)), CLCN6 (P = 4.79×10(-9)), FURIN (P = 1.38×10(-6)), HECTD4 (P = 3.95×10(-11)), NPPA (P = 1.60×10(-6)), and PTPN11 (P = 8.89×10(-10)) in the genetic basis of blood pressure. The present study found some important genes associated with blood pressure, which might provide insights into the genetic architecture of hypertension.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyAlgorithmsBlood PressureCluster AnalysisCoronary Artery DiseaseDiastoleGenetic PleiotropyHumansMolecular Sequence AnnotationProtein Interaction MapsSoftwareSystoleblood pressurecoronary artery diseasegene-based associationgenome-wide association studyprotein–protein interaction

Identifiers

PMID25820152
PMCPMC4478534

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.