Evidence map›Paper›PMID 25519336›Full record

ArticleBMC proceedings2014

A powerful statistical method identifies novel loci associated with diastolic blood pressure triggered by nonlinear gene-environment interaction.

Honglang Wang, Tao He, Cen Wu, Ping-Shou Zhong, Yuehua Cui

Open access · goldAbstract read
In one paragraph

Article in BMC proceedings, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 36% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 1 institution in 1 country.

Honglang WangDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Rd. Rm C432, East Lansing, MI 48824, USA.
Tao HeDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Rd. Rm C432, East Lansing, MI 48824, USA.
Cen WuDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Rd. Rm C432, East Lansing, MI 48824, USA.
Ping-Shou ZhongDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Rd. Rm C432, East Lansing, MI 48824, USA.
Yuehua CuiDepartment of Statistics & Probability, Michigan State University, 619 Red Cedar Rd. Rm C432, East Lansing, MI 48824, USA.
Michigan State University · US

Funding

GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
NIGMS NIH HHS R01 GM031575
6 · The paper itself

Abstract

The genetic basis of blood pressure often involves multiple genetic factors and their interactions with environmental factors. Gene-environment interaction is assumed to play an important role in determining individual blood pressure variability. Older people are more prone to high blood pressure than younger ones and the risk may not display a linear trend over the life span. However, which gene shows sensitivity to aging in its effect on blood pressure is not clear. In this work, we allowed the genetic effect to vary over time and propose a varying-coefficient model to identify potential genetic players that show nonlinear response across different age stages. We detected 2 novel loci, gene MIR1263 (a microRNA coding gene) on chromosome 3 and gene UNC13B on chromosome 9, that are nonlinearly associated with diastolic blood pressure. Further experimental validation is needed to confirm this finding.

Identifiers

PMID25519336
PMCPMC4143702
OpenAlexW2089752852

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.