Evidence map›Paper›PMID 27980650›Full record

ArticleBMC proceedings2016

Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure.

Hongbao Cao, Wei Guo, Haide Qin, Mengyuan Xu, Benjamin Lehrman, Yu Tao, Yin-Yao Shugart

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

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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

7 authors.

Hongbao CaoUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Wei GuoUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Haide QinUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Mengyuan XuUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Benjamin LehrmanUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Yu TaoUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Yin-Yao ShugartUnit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.

Funding

GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Statistical Genomics and Data Analysis CoreZIAMH002965 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI YAO, YIN · 2017 to 2017
$856k
NIGMS NIH HHS R01 GM031575
6 · The paper itself

Abstract

backgroundAlthough many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection.

methodsApplying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data.

resultsAmong the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. In addition, we identified 173 variables that were associated with body weight and 84 associated with left ventricular contractility. Together, 55.9 % of the top 1000 variables showed associations with blood pressure related phenotypes(SNP/gene expression =348/211).

conclusionsOur results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis.

Identifiers

PMID27980650
PMCPMC5133507

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