Evidence map›Paper›PMID 23345460›Full record

ArticleGenome research2013

A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines.

Liming Liang, Nilesh Morar, Anna L Dixon, G Mark Lathrop, Goncalo R Abecasis, Miriam F Moffatt, William O C Cookson

Abstract read
In one paragraph

Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
91citing papers in PubMed, 12 pooled it
–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

91 citing papers in PubMed, 12 syntheses or guidelines pooled it.

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31 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

7 authors.

Liming LiangDepartment of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Nilesh Morar
Anna L Dixon
G Mark Lathrop
Goncalo R Abecasis
Miriam F Moffatt
William O C Cookson

Funding

Medical Research CouncilWellcome Trust 097117
6 · The paper itself

Abstract

Gene expression levels can be an important link DNA between variation and phenotypic manifestations. Our previous map of global gene expression, based on ~400K single nucleotide polymorphisms (SNPs) and 50K transcripts in 400 sib pairs from the MRCA family panel, has been widely used to interpret the results of genome-wide association studies (GWASs). Here, we more than double the size of our initial data set with expression data on 550 additional individuals from the MRCE family panel using the Illumina whole-genome expression array. We have used new statistical methods for dimension reduction to account for nongenetic effects in estimates of expression levels, and we have also included SNPs imputed from the 1000 Genomes Project. Our methods reduced false-discovery rates and increased the number of expression quantitative trait loci (eQTLs) mapped either locally or at a distance (i.e., in cis or trans) from 1534 in the MRCA data set to 4452 (with <5% FDR). Imputation of 1000 Genomes SNPs further increased the number of eQTLs to 7302. Using the same methods and imputed SNPs in the newly acquired MRCE data set, we identified eQTLs for 9000 genes. The combined results identify strong local and distant effects for transcripts from 14,177 genes. Our eQTL database based on these results is freely available to help define the function of disease-associated variants.

Indexed as

Gene ExpressionGenome-Wide Association StudyQuantitative Trait LociChromosome MappingComputational BiologyDatabases, Nucleic AcidGene Expression ProfilingGenomicsGenotypeHumansInternetLymphocytesPolymorphism, Single NucleotideSiblings

Identifiers

PMID23345460
PMCPMC3613588

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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.