Evidence map›Paper›PMID 27559110›Full record

ArticleHuman molecular genetics2016

Collapsed methylation quantitative trait loci analysis for low frequency and rare variants.

Tom G Richardson, Hashem A Shihab, Gibran Hemani, Jie Zheng, Eilis Hannon, Jonathan Mill, Elena Carnero-Montoro, Jordana T Bell, Oliver Lyttleton, Wendy L McArdle and 7 more

Open access · hybridAbstract read
In one paragraph

Article in Human molecular genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.5field-weighted citation impact, top 28% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

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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

17 authors at 3 institutions in 1 country.

Tom G RichardsonMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Hashem A ShihabMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Gibran HemaniMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Jie ZhengMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Eilis HannonUniversity of Exeter Medical School, University of Exeter, Exeter, UK.
Jonathan MillUniversity of Exeter Medical School, University of Exeter, Exeter, UK.
Elena Carnero-MontoroDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Jordana T BellDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Oliver LyttletonAvon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Bristol, UK.
Wendy L McArdleAvon Longitudinal Study of Parents and Children (ALSPAC) & School of Social and Community Medicine, University of Bristol, Bristol, UK.
Susan M RingMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Santiago RodriguezBristol Genetic Epidemiology Laboratories, School of Social and Community Medicine, University of Bristol, Bristol, UK.
Colin CampbellIntelligent Systems Laboratory, University of Bristol, Bristol, UK.
George Davey SmithMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Caroline L ReltonMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Nicholas J TimpsonMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK.
Tom R GauntMRC Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK Tom.Gaunt@bristol.ac.uk.
University of Bristol · GBKing's College London · GBUniversity of Exeter · GB

Funding

Biotechnology and Biological Sciences Research Council BB/I025263/1Biotechnology and Biological Sciences Research Council BB/I025751/1Medical Research Council G9815508Medical Research Council MC_PC_15018Medical Research Council MC_UU_12013/1Medical Research Council MC_UU_12013/3Medical Research Council MR/K013807/1Wellcome Trust 102215Wellcome Trust 202802/Z/16/Z
6 · The paper itself

Abstract

backgroundSingle variant approaches have been successful in identifying DNA methylation quantitative trait loci (mQTL), although as with complex traits they lack the statistical power to identify the effects from rare genetic variants. We have undertaken extensive analyses to identify regions of low frequency and rare variants that are associated with DNA methylation levels.

methodsWe used repeated measurements of DNA methylation from five different life stages in human blood, taken from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Variants were collapsed across CpG islands and their flanking regions to identify variants collectively associated with methylation, where no single variant was individually responsible for the observed signal. All analyses were undertaken using the sequence kernel association test.

resultsFor loci where no individual variant mQTL was observed based on a single variant analysis, we identified 95 unique regions where the combined effect of low frequency variants (MAF ≤ 5%) provided strong evidence of association with methylation. For loci where there was previous evidence of an individual variant mQTL, a further 3 regions provided evidence of association between multiple low frequency variants and methylation levels. Effects were observed consistently across 5 different time points in the lifecourse and evidence of replication in the TwinsUK and Exeter cohorts was also identified.

conclusionWe have demonstrated the potential of this novel approach to mQTL analysis by analysing the combined effect of multiple low frequency or rare variants. Future studies should benefit from applying this approach as a complementary follow up to single variant analyses.

Indexed as

Genome-Wide Association StudyAdolescentAdultChildChild, PreschoolCpG IslandsDNA MethylationFemaleGene Expression RegulationGene FrequencyGenetic VariationGenotypeHumansInfantInfant, NewbornMale

Identifiers

PMID27559110
PMCPMC5291201
OpenAlexW2517281723

What OpenQuestion holds

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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.