Evidence map›Paper›PMID 32218440›Full record

ArticleNature communications2020

Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.

Saori Sakaue, Jun Hirata, Masahiro Kanai, Ken Suzuki, Masato Akiyama, Chun Lai Too, Thurayya Arayssi, Mohammed Hammoudeh, Samar Al Emadi, Basel K Masri and 10 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
13.0field-weighted citation impact, top 1% of its field
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

50 citing papers in PubMed, 118 citations in OpenAlex.

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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

20 authors at 15 institutions in 9 countries.

Saori SakaueDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.ORCID http://orcid.org/0000-0003-3618-9717
Jun HirataDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
Masahiro KanaiDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.ORCID http://orcid.org/0000-0001-5165-4408
Ken SuzukiDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.ORCID http://orcid.org/0000-0003-1065-3593
Masato AkiyamaLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Chun Lai TooAllergy and Immunology Research Center, Institute for Medical Research, Ministry of Health Malaysia, 40170, Setia Alam, Malaysia.ORCID http://orcid.org/0000-0002-4079-8454
Thurayya ArayssiDepartment of Internal Medicine, Weill Cornell Medicine-Qatar, Education City, Doha, 24144, Qatar.
Mohammed HammoudehDepartment of Internal Medicine, Hamad Medical Corporation, Doha, 3050, Qatar.
Samar Al EmadiDepartment of Internal Medicine, Hamad Medical Corporation, Doha, 3050, Qatar.
Basel K MasriDepartment of Internal Medicine, Jordan Hospital, Amman, 520248, Jordan.
Hussein HalabiRheumatology Division, Department of Internal Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, H45X+P6, Saudi Arabia.
Humeira BadshaDr. Humeira Badsha Medical Center, Emirates Hospital, Dubai, 391203, United Arab Emirates.
Imad W UthmanDepartment of Rheumatology, American University of Beirut, Beirut, 11-0236, Lebanon.ORCID http://orcid.org/0000-0001-8285-6483
Richa SaxenaCenter for Genomic Medicine, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02115, USA.ORCID http://orcid.org/0000-0003-2233-1065
Leonid PadyukovDivision of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital, 17177, Stockholm, Sweden.ORCID http://orcid.org/0000-0003-2950-5670
Makoto HirataLaboratory of Genome Technology, Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan.ORCID http://orcid.org/0000-0002-9994-9958
Koichi MatsudaDepartment of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, the University of Tokyo, Tokyo, 108-8639, Japan.ORCID http://orcid.org/0000-0001-7292-2686
Yoshinori MurakamiDivision of Molecular Pathology, Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan.ORCID http://orcid.org/0000-0002-2826-4396
Yoichiro KamataniLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.ORCID http://orcid.org/0000-0001-8748-5597
Yukinori OkadaDepartment of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan. yokada@sg.med.osaksa-u.ac.jp.ORCID http://orcid.org/0000-0002-0311-8472
The University of Tokyo · JPHamad Medical Corporation · QARIKEN Center for Integrative Medical Sciences · JPThe University of Osaka · JPAmerican University of Beirut · LBBroad Institute · USDr. Humeira Badsha Medical Center · AEHarvard University · USJordan Hospital · JOKarolinska University Hospital · SEKing Faisal Specialist Hospital & Research Centre · SAKyushu University · JPMinistry of Health · MYTeijin (Japan) · JPWeill Cornell Medical College in Qatar · QA

Funding

EPA EP-C-17-017
6 · The paper itself

Abstract

The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on complex traits. Here we apply dimensionality reduction methods (PCA, t-SNE, PCA-t-SNE, UMAP, and PCA-UMAP) to biobank-derived genomic data of a Japanese population (n = 169,719). Dimensionality reduction reveals fine-scale population structure, conspicuously differentiating adjacent insular subpopulations. We further enluciate the demographic landscape of these Japanese subpopulations using population genetics analyses. Finally, we perform phenome-wide polygenic risk score (PRS) analyses on 67 complex traits. Differences in PRS between the deconvoluted subpopulations are not always concordant with those in the observed phenotypes, suggesting that the PRS differences might reflect biases from the uncorrected structure, in a trait-dependent manner. This study suggests that such an uncorrected structure can be a potential pitfall in the clinical application of PRS.

Indexed as

Genetic Predisposition to DiseaseGenetics, PopulationMultifactor Dimensionality ReductionAsian PeopleBase SequenceBiological Specimen BanksHumansJapanMultifactorial InheritancePhenotypePrincipal Component AnalysisRisk Factors

Identifiers

PMID32218440
PMCPMC7099015
OpenAlexW3013112475

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.