Evidence map›Paper›PMID 30407537›Full record

ArticleNucleic acids research2018

Integration of genetics and miRNA-target gene network identified disease biology implicated in tissue specificity.

Saori Sakaue, Jun Hirata, Yuichi Maeda, Eiryo Kawakami, Takuro Nii, Toshihiro Kishikawa, Kazuyoshi Ishigaki, Chikashi Terao, Ken Suzuki, Masato Akiyama and 25 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Association of theAnnals of the rheumatic diseases · 2020
    Pooled it
  2. Pooled it
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  4. Article
  5. Noncanonical functions of microRNAs in the nucleus.Acta biochimica et biophysica Sinica · 2024
    Review
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  7. Article
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  10. Comprehensive Analysis of the Effect of 20(Molecules (Basel, Switzerland) · 2022
    Article
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  13. MicroRNA-3148 inhibits glioma by decreasingExperimental and therapeutic medicine · 2022
    Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
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

35 authors.

Saori SakaueDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Jun HirataDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Yuichi MaedaDepartment of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.
Eiryo KawakamiHealthcare and Medical Data Driven AI based Predictive Reasoning Development Unit, Medical Sciences Innovation Hub Program (MIH), RIKEN, Yokohama 230-0045, Japan.
Takuro NiiDepartment of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.
Toshihiro KishikawaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Kazuyoshi IshigakiLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Chikashi TeraoLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Ken SuzukiDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Masato AkiyamaLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Naomasa SuitaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Tatsuo MasudaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Kotaro OgawaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Kenichi YamamotoDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Yukihiko SaekiDepartment of Clinical Research, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan.
Masato MatsushitaDepartment of Rheumatology and Allergology, Saiseikai Senri Hospital, Suita 565-0862, Japan.
Maiko YoshimuraDepartment of Rheumatology and Allergology, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan.
Hidetoshi MatsuokaDepartment of Rheumatology and Allergology, National Hospital Organization (NHO) Osaka Minami Medical Center, Japan.
Katsunori IkariInstitute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan.
Atsuo TaniguchiInstitute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan.
Hisashi YamanakaInstitute of Rheumatology, Tokyo Women's Medical University, Tokyo 162-0054, Japan.
Hideya KawajiDivision of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.
Timo LassmannDivision of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.
Masayoshi ItohDivision of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.
Hiroyuki YoshitomiLaboratory of Tissue Regeneration, Department of Regeneration Science and Engineering, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto 606-8507, Japan.
Hiromu ItoDepartment of Orthopaedic Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
Koichiro OhmuraDepartment of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan.
Alistair R R ForrestDivision of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.
Yoshihide HayashizakiRIKEN Omics Science Center (OSC), Yokohama 230-0045, Japan.
Piero CarninciDivision of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.
Atsushi KumanogohDepartment of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, WPI Immunology Frontier Research Center, Osaka University, Suita 565-0871, Japan.
Yoichiro KamataniLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Michiel de HoonDivision of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama 230-0045, Japan.
Kazuhiko YamamotoLaboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan.
Yukinori OkadaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA-target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA-target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10-8). Our result highlighted that miRNA-target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.

Indexed as

Gene Regulatory NetworksGenome, HumanAlgorithmsArthritis, RheumatoidAsthmaBiomarkersCase-Control StudiesColitis, UlcerativeComputational BiologyGene Expression ProfilingGene Expression RegulationGenetic LociGenome-Wide Association StudyGraves DiseaseHumansMicroRNAsBiomarkersMicroRNAs

Identifiers

PMID30407537
PMCPMC6294505

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.