Evidence map›Paper›PMID 37915762›Full record

ArticleNAR genomics and bioinformatics2023

Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.

Qingbo S Wang, Ryuya Edahiro, Ho Namkoong, Takanori Hasegawa, Yuya Shirai, Kyuto Sonehara, Japan COVID-19 Task Force, Atsushi Kumanogoh, Makoto Ishii, Ryuji Koike and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

17 authors.

Qingbo S WangDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.ORCID https://orcid.org/0000-0002-9110-5830
Ryuya EdahiroDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
Ho NamkoongDepartment of Infectious Diseases, Keio University School of Medicine, Tokyo, 160-8582, Japan.ORCID https://orcid.org/0000-0001-6181-4284
Takanori HasegawaM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
Yuya ShiraiDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
Kyuto SoneharaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
Japan COVID-19 Task Force
Atsushi KumanogohDepartment of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
Makoto IshiiDepartment of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 tsurumai, Showa-ku, Nagoya, 466-8550, Japan.ORCID https://orcid.org/0000-0003-4396-1664
Ryuji KoikeHealth Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
Akinori KimuraInstitute of Research, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
Seiya ImotoDivision of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan.ORCID https://orcid.org/0000-0002-2989-308X
Satoru MiyanoM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, 113-8510, Japan.
Seishi OgawaDepartment of Pathology and Tumor Biology, Kyoto University, Kyoto, 606-8315, Japan.ORCID https://orcid.org/0000-0002-7778-5374
Takanori KanaiDivision of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Koichi FukunagaDivision of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Yukinori OkadaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.ORCID https://orcid.org/0000-0002-0311-8472

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Statistical fine-mapping prioritizes putative causal variants from a large number of candidate variants, and is widely used in expression quantitative loci (eQTLs) studies. In eQTL fine-mapping, the existence of causal variants for gene expression is not guaranteed, since the genetic heritability of gene expression explained by nearby (

Identifiers

PMID37915762
PMCPMC10616627

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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.