Evidence map›Paper›PMID 26391122›Full record

ArticleGenome medicine2015

Omic personality: implications of stable transcript and methylation profiles for personalized medicine.

Rubina Tabassum, Ambily Sivadas, Vartika Agrawal, Haozheng Tian, Dalia Arafat, Greg Gibson

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
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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 at 1 institution in 3 countries.

Rubina TabassumCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building 1-96, 770 State Street, Atlanta, GA, 30332, USA. rubina.tabassum@helsinki.fi.
Ambily SivadasCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building 1-96, 770 State Street, Atlanta, GA, 30332, USA. Ambily.sivadas@gmail.com.
Vartika AgrawalCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building 1-96, 770 State Street, Atlanta, GA, 30332, USA. vartikaagrawal198@gmail.com.
Haozheng TianCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building 1-96, 770 State Street, Atlanta, GA, 30332, USA. tianhzh@gatech.edu.
Dalia ArafatCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building 1-96, 770 State Street, Atlanta, GA, 30332, USA. darafat.gt@gmail.com.
Greg GibsonCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Boggs Building 1-96, 770 State Street, Atlanta, GA, 30332, USA. greg.gibson@biology.gatech.edu.
Georgia Institute of Technology · US

Funding

Statistical Methods for X-Chromosome Genetic DataP01GM099568 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2012 to 2016
$7.3M
NIGMS NIH HHS P01 GM099568NIGMS NIH HHS P01-GM0996568
6 · The paper itself

Abstract

backgroundPersonalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes.

methodsWhole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness.

resultsLongitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression.

conclusionsPeople express an "omic personality" consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.

Indexed as

DNA MethylationGene Expression ProfilingGenomicsAdultBlack or African AmericanCpG IslandsFemaleHumansMaleMicroRNAsMiddle AgedPilot ProjectsPrecision MedicineSequence Analysis, RNATranscription, GeneticWhite PeopleMicroRNAs

Identifiers

PMID26391122
PMCPMC4578259
OpenAlexW2114151615

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.