Evidence map›Paper›PMID 26699738›Full record

ArticleGenome biology2015

High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction.

Joseph L McClay, Andrey A Shabalin, Mikhail G Dozmorov, Daniel E Adkins, Gaurav Kumar, Srilaxmi Nerella, Shaunna L Clark, Sarah E Bergen, Swedish Schizophrenia Consortium, Christina M Hultman and 4 more

Open access · goldAbstract read
In one paragraph

Article in Genome biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 3 pooled it
7.5field-weighted citation impact, top 2% 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

89 citing papers in PubMed, 3 syntheses or guidelines pooled it, 148 citations in OpenAlex.

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29 more citing papers are in PubMed but not listed here.

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

14 authors at 4 institutions in 2 countries.

Joseph L McClayDepartment of Pharmacotherapy and Outcomes Science, Virginia Commonwealth University, 410 North 12th Street, Richmond, VA, 23298, USA. jlmcclay@vcu.edu.
Andrey A ShabalinCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA. ashabalin@vcu.edu.
Mikhail G DozmorovDepartment of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.
Daniel E AdkinsCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA.
Gaurav KumarCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA.
Srilaxmi NerellaCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA.
Shaunna L ClarkCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA.
Sarah E BergenDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Swedish Schizophrenia Consortium
Christina M HultmanDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Patrik K E MagnussonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Patrick F SullivanDepartment of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
Karolina A AbergCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA.
Edwin J C G van den OordCenter for Biomarker Research and Precision Medicine, Virginia Commonwealth University, 1112 East Clay Street, Richmond, VA, 23298, USA.
Virginia Commonwealth University · USKarolinska Institutet · SEBroad Institute · USUniversity of North Carolina at Chapel Hill · US

Funding

A Large-Scale Schizophrenia Association Study in SwedenR01MH077139 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SULLIVAN, PATRICK F · 2007 to 2018
$9.9M
1/2-Cis & Trans-Data Integration to Find Mechanisms Causing Psychiatric DisorderR01MH097283 · NIMH · VIRGINIA COMMONWEALTH UNIVERSITY · PI VAN DEN OORD, EDWIN · 2012 to 2014
$996k
Integrating Genomic and Environmental Perspectives on Internalizing DisordersK01MH093731 · NIMH · VIRGINIA COMMONWEALTH UNIVERSITY · PI ADKINS, DANIEL E · 2011 to 2015
$649k
Investigating Methylation Patterns Associated with Alcohol Use and AddictionK01AA021266 · NIAAA · VIRGINIA COMMONWEALTH UNIVERSITY · PI CLARK, SHAUNNA L · 2012 to 2016
$580k
Mediators of methylomic profiles in 1500 schizophrenia cases and controlsR03MH102723 · NIMH · VIRGINIA COMMONWEALTH UNIVERSITY · PI ABERG, KAROLINA ANNA · 2014 to 2015
$153k
NIAAA NIH HHS K01 AA021266NIMH NIH HHS K01 MH093731NIMH NIH HHS R01 MH077139NIMH NIH HHS R01 MH097283NIMH NIH HHS R03 MH102723
6 · The paper itself

Abstract

backgroundGenetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTLs).

resultsUsing stringent false discovery rate control, 15 % of methylation sites show genetic influence. Most meQTLs are local, where the associated SNP and methylation site are in close genomic proximity. Distant meQTLs and those spanning different chromosomes are less common. Most local meQTLs encompass common SNPs that alter CpG sites (CpG-SNPs). Local meQTLs encompassing CpG-SNPs are enriched in regions of inactive chromatin in blood cells. In contrast, local meQTLs lacking CpG-SNPs are enriched in regions of active chromatin and transcription factor binding sites. Of 393 local meQTLs that overlap disease-associated regions from genome-wide studies, a high percentage encompass common CpG-SNPs. These meQTLs overlap active enhancers, differentiating them from CpG-SNP meQTLs in inactive chromatin.

conclusionsGenetic influence on the human blood methylome is common, involves several heterogeneous processes and is predominantly dependent on local sequence context at the meQTL site. Most meQTLs involve CpG-SNPs, while sequence-dependent effects on chromatin binding are also important in regions of active chromatin. An abundance of local meQTLs resulting from methylation of CpG-SNPs in inactive chromatin suggests that many meQTLs lack functional consequence. Integrating meQTL and Roadmap Epigenomics data could assist fine-mapping efforts.

Indexed as

DNA MethylationGenome, HumanQuantitative Trait LociBloodCpG IslandsFemaleHigh-Throughput Nucleotide SequencingHumansMalePolymorphism, Single NucleotideSequence Analysis, DNA

Identifiers

PMID26699738
PMCPMC4699364
OpenAlexW2209701692

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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.