Evidence map›Paper›PMID 28122634›Full record

ArticleGenome biology2017

Integrated genome-wide analysis of expression quantitative trait loci aids interpretation of genomic association studies.

Roby Joehanes, Xiaoling Zhang, Tianxiao Huan, Chen Yao, Sai-Xia Ying, Quang Tri Nguyen, Cumhur Yusuf Demirkale, Michael L Feolo, Nataliya R Sharopova, Anne Sturcke and 14 more

Abstract read
In one paragraph

Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed, 11 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

119 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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  12. Article
  13. Multi-omics integration andFrontiers in immunology · 2026
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  19. SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.Animals : an open access journal from MDPI · 2024
    Article
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59 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

24 authors.

Roby JoehanesThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Xiaoling ZhangThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Tianxiao HuanThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Chen YaoThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Sai-Xia YingMathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.
Quang Tri NguyenMathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.
Cumhur Yusuf DemirkaleMathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA.
Michael L FeoloNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Nataliya R SharopovaNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Anne SturckeNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Alejandro A SchäfferNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Nancy Heard-CostaDepartment of Neurology, Boston University School of Medicine, Boston, MA, USA.
Han ChenSchool of Public Health, Harvard University, Boston, MA, USA.
Po-Ching LiuDNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA.
Richard WangDNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA.
Kimberly A WoodhouseDNA Sequencing and Genomics Core, National Institutes of Health, Bethesda, MD, USA.
Kahraman TanriverdiDepartment of Medicine, University of Massachusetts Medical School, Worchester, MA, USA.
Jane E FreedmanDepartment of Medicine, University of Massachusetts Medical School, Worchester, MA, USA.
Nalini RaghavachariNational Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Josée DupuisThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Andrew D JohnsonThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Christopher J O'DonnellThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA.
Daniel LevyThe Framingham Heart Study and the Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA. levyd@nhlbi.nih.gov.
Peter J MunsonMathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA. munson@mail.nih.gov.ORCID 0000-0003-0824-7887

Funding

Framingham Project/LevyZIAHL006001 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI LEVY, DANIEL · 2009 to 2025
$51.1M
Cardiovascular Genomics and Bioinformatics projectsZIAHL006170 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI JOHNSON, ANDREW · 2013 to 2025
$25.3M
Multiethnic Study of Type 2 Diabetes GenesU01DK085526 · NIDDK · BROAD INSTITUTE, INC. · PI ALTSHULER, DAVID, JABLONSKI, KATHLEEN ANN · 2009 to 2014
$15.7M
Common Genetic Variation and Quantitative Diabetes TraitsR01DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2008 to 2014
$5.0M
Case Studies In GeneticsZIALM000097 · NLM · NATIONAL LIBRARY OF MEDICINE · PI SCHAFFER, ALEJANDRO A · 2009 to 2019
$4.6M
Rare Sequence Variation and Diabetes Quantitative TraitsU01DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2015 to 2019
$3.6M
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
–
NHLBI NIH HHS N01 HC025195NIDDK NIH HHS R01 DK078616NIDDK NIH HHS U01 DK078616NIDDK NIH HHS U01 DK085526
6 · The paper itself

Abstract

backgroundIdentification of single nucleotide polymorphisms (SNPs) associated with gene expression levels, known as expression quantitative trait loci (eQTLs), may improve understanding of the functional role of phenotype-associated SNPs in genome-wide association studies (GWAS). The small sample sizes of some previous eQTL studies have limited their statistical power. We conducted an eQTL investigation of microarray-based gene and exon expression levels in whole blood in a cohort of 5257 individuals, exceeding the single cohort size of previous studies by more than a factor of 2.

resultsWe detected over 19,000 independent lead cis-eQTLs and over 6000 independent lead trans-eQTLs, targeting over 10,000 gene targets (eGenes), with a false discovery rate (FDR) < 5%. Of previously published significant GWAS SNPs, 48% are identified to be significant eQTLs in our study. Some trans-eQTLs point toward novel mechanistic explanations for the association of the SNP with the GWAS-related phenotype. We also identify 59 distinct blocks or clusters of trans-eQTLs, each targeting the expression of sets of six to 229 distinct trans-eGenes. Ten of these sets of target genes are significantly enriched for microRNA targets (FDR < 5%). Many of these clusters are associated in GWAS with multiple phenotypes.

conclusionsThese findings provide insights into the molecular regulatory patterns involved in human physiology and pathophysiology. We illustrate the value of our eQTL database in the context of a recent GWAS meta-analysis of coronary artery disease and provide a list of targeted eGenes for 21 of 58 GWAS loci.

Indexed as

Gene ExpressionGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsQuantitative Trait LociAdultAgedAllelesCluster AnalysisFemaleGene Expression ProfilingGene FrequencyHumansMaleMicroRNAsMiddle AgedMicroRNAs

Identifiers

PMID28122634
PMCPMC5264466

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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.