Evidence map›Paper›PMID 25519350›Full record

ArticleBMC proceedings2014

Testing for associations between systolic blood pressure and single-nucleotide polymorphism profiles obtained from sparse principal component analysis.

Ashley Bonner, Binod Neupane, Joseph Beyene

Abstract 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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Ashley BonnerClinical Epidemiology and Biostatistics Department, McMaster University, Hamilton, Ontario, Canada L8S 4L8.
Binod NeupaneClinical Epidemiology and Biostatistics Department, McMaster University, Hamilton, Ontario, Canada L8S 4L8.
Joseph BeyeneClinical Epidemiology and Biostatistics Department, McMaster University, Hamilton, Ontario, Canada L8S 4L8.

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

backgroundHypertension is a prevalent condition linked to major cardiovascular conditions and multiple other comorbidities. Genetic information can offer a deeper understanding about susceptibility and the underlying disease mechanisms. The Genetic Analysis Workshop 18 (GAW18) provides abundant genotype data to determine genetic associations for being hypertensive and for the underlying trait of systolic blood pressure (SBP). The high-dimensional nature of this data promotes dimension reduction techniques to remove excess noise and also synthesize genetic information for complex, polygenic traits.

methodsFor both measured and simulated phenotype data from GAW18, we use sparse principal component analysis to obtain sparse genetic profiles that represent the underlying data structures. We then detect associations between the obtained sparse principal components (PCs) and SBP, a major indicator of hypertension, following up by investigating the sparse PCs for genetic structure to gain insight into new patterns.

resultsAfter adjusting for multiple testing, 27 of 122 PCs were significantly associated with measured SBP, offering a large number of components to investigate. Considering the top 3 PCs, linked genetic regions have been identified; these may act in unison while associated with SBP. Simulated data offered similar results.

conclusionsSparse PCs can offer a new data-driven approach to structuring genotype data and understanding the genetic mechanics behind complex, polygenic traits such as hypertension.

Identifiers

PMID25519350
PMCPMC4143624

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.