Evidence map›Paper›PMID 39702642›Full record

ArticleCommunications biology2024

Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese population.

Jack Flanagan, Xiaoxi Liu, David Ortega-Reyes, Kohei Tomizuka, Nana Matoba, Masato Akiyama, Masaru Koido, Kazuyoshi Ishigaki, Kyota Ashikawa, Sadaaki Takata and 11 more

Abstract read
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
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  10. Unraveling time-dependent genetic components underlying alcohol response.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025
    Article
  11. Review
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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

21 authors.

Jack Flanagan *Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Xiaoxi Liu *Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0002-1933-8613
David Ortega-Reyes *Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Kohei TomizukaLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Nana MatobaLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0001-5329-0134
Masato AkiyamaLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Masaru KoidoLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0002-0348-0666
Kazuyoshi IshigakiLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0003-2881-0657
Kyota AshikawaLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Sadaaki TakataLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0002-5354-5341
MingYang ShiLaboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Tomomi AoiLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Yukihide MomozawaLaboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0001-5638-3504
Kaoru ItoLaboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.ORCID 0000-0003-1843-773X
Yoshinori MurakamiDivision of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-2826-4396
Koichi MatsudaLaboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Biobank Japan Project
Yoichiro KamataniLaboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Andrew P MorrisDepartment of Biostatistics, University of Liverpool, Liverpool, UK.ORCID 0000-0002-6805-6014
Momoko HorikoshiLaboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. momoko.horikoshi@riken.jp.
Chikashi TeraoLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. chikashi.terao@riken.jp.ORCID 0000-0002-6452-4095

Funding

Japan Agency for Medical Research and Development (AMED) 21ck0106642Japan Agency for Medical Research and Development (AMED) 22ek0109555Japan Agency for Medical Research and Development (AMED) 23ek0410114Japan Agency for Medical Research and Development (AMED) 23tm0424225Japan Agency for Medical Research and Development (AMED) JP18km0605001
6 · The paper itself

Abstract

To improve imputation quality for genome-wide association studies (GWAS) conducted on the Japanese population, we developed and evaluated four Japanese population-specific reference panels. These panels were constructed through the augmentation of the 1000 Genomes Project (1KG) panel using Japanese whole genome sequencing (WGS) data, with sample sizes ranging from 1 K to 7 K individuals enrolled through the Biobank Japan (BBJ) project, and sequencing depths ranging from 3× to 30×. Among these panels, an augmented reference panel comprising 7472 WGS samples of mixed depth (1KG+7K) exhibit the greatest improvement in imputation quality relative to the Trans-Omics for Precision Medicine (TOPMed) reference panel. Notably, we observe these improvements primarily for rare variants with a minor allele frequency (MAF) <5%. To demonstrate the benefits of improved imputation quality in association analyses of complex traits, we conducted GWAS for serum uric acid and total cholesterol levels following imputation up to the 1KG+7K panel. The analysis reveals several loci reaching genome-wide significance (P < 5 × 10

Indexed as

East Asian PeopleGenome-Wide Association StudyGene FrequencyGenome, HumanHumansJapanPolymorphism, Single NucleotideWhole Genome Sequencing

Identifiers

PMID39702642
PMCPMC11659288

What OpenQuestion holds

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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.