Evidence map›Paper›PMID 18974877›Full record

ArticlePLoS genetics2008

Modifier effects between regulatory and protein-coding variation.

Antigone S Dimas, Barbara E Stranger, Claude Beazley, Robert D Finn, Catherine E Ingle, Matthew S Forrest, Matthew E Ritchie, Panos Deloukas, Simon Tavaré, Emmanouil T Dermitzakis

Abstract read
In one paragraph

Article in PLoS genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Review
  2. A 20 Bp Indel ofAnimals : an open access journal from MDPI · 2026
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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

10 authors.

Antigone S DimasWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.
Barbara E Stranger
Claude Beazley
Robert D Finn
Catherine E Ingle
Matthew S Forrest
Matthew E Ritchie
Panos Deloukas
Simon Tavaré
Emmanouil T Dermitzakis

Funding

Wellcome Trust
6 · The paper itself

Abstract

Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

Indexed as

Epistasis, GeneticPolymorphism, Single NucleotideRegulatory Sequences, Nucleic AcidAmino Acid SubstitutionAnalysis of VarianceChromosome MappingGene ExpressionGenome, HumanGenotypeHumansLinkage DisequilibriumOpen Reading FramesTranscriptional Activation

Identifiers

PMID18974877
PMCPMC2570624

What OpenQuestion holds

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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.