Evidence map›Paper›PMID 23755072›Full record

ArticleFrontiers in genetics2013

Using eQTL weights to improve power for genome-wide association studies: a genetic study of childhood asthma.

Lin Li, Michael Kabesch, Emmanuelle Bouzigon, Florence Demenais, Martin Farrall, Miriam F Moffatt, Xihong Lin, Liming Liang

Abstract read
In one paragraph

Article in Frontiers in genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 3 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

38 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Multi-omics Integrative Analysis for Incomplete Data Using WeightedJournal of agricultural, biological, and environmental statistics · 2025
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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

8 authors.

Lin LiDepartment of Biostatistics, Harvard School of Public Health Boston, MA, USA.
Michael Kabesch
Emmanuelle Bouzigon
Florence Demenais
Martin Farrall
Miriam F Moffatt
Xihong Lin
Liming Liang

Funding

Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Statistical Methods for Correlated and High-Dimensional Biomedical DataR37CA076404 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG · 2007 to 2014
$2.5M
NCI NIH HHS P01 CA134294NCI NIH HHS R37 CA076404Wellcome Trust 090532Wellcome Trust 097117
6 · The paper itself

Abstract

Increasing evidence suggests that single nucleotide polymorphisms (SNPs) associated with complex traits are more likely to be expression quantitative trait loci (eQTLs). Incorporating eQTL information hence has potential to increase power of genome-wide association studies (GWAS). In this paper, we propose using eQTL weights as prior information in SNP based association tests to improve test power while maintaining control of the family-wise error rate (FWER) or the false discovery rate (FDR). We apply the proposed methods to the analysis of a GWAS for childhood asthma consisting of 1296 unrelated individuals with German ancestry. The results confirm that eQTLs are enriched for previously reported asthma SNPs. We also find that some SNPs are insignificant using procedures without eQTL weighting, but become significant using eQTL-weighted Bonferroni or Benjamini-Hochberg procedures, while controlling the same FWER or FDR level. Some of these SNPs have been reported by independent studies in recent literature. The results suggest that the eQTL-weighted procedures provide a promising approach for improving power of GWAS. We also report the results of our methods applied to the large-scale European GABRIEL consortium data.

Indexed as

asthmaeQTLfalse discovery ratefamily-wise error rategenome-wide association studyweighted hypothesis test

Identifiers

PMID23755072
PMCPMC3668139

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Registered trials

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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.