Affiliations 

  • 1 Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan
  • 2 Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
  • 3 Allergy and Immunology Research Center, Institute for Medical Research, Ministry of Health Malaysia, 40170, Setia Alam, Malaysia
  • 4 Department of Internal Medicine, Weill Cornell Medicine-Qatar, Education City, Doha, 24144, Qatar
  • 5 Department of Internal Medicine, Hamad Medical Corporation, Doha, 3050, Qatar
  • 6 Department of Internal Medicine, Jordan Hospital, Amman, 520248, Jordan
  • 7 Rheumatology Division, Department of Internal Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, H45X+P6, Saudi Arabia
  • 8 Dr. Humeira Badsha Medical Center, Emirates Hospital, Dubai, 391203, United Arab Emirates
  • 9 Department of Rheumatology, American University of Beirut, Beirut, 11-0236, Lebanon
  • 10 Center for Genomic Medicine, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02115, USA
  • 11 Division of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital, 17177, Stockholm, Sweden
  • 12 Laboratory of Genome Technology, Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan
  • 13 Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, the University of Tokyo, Tokyo, 108-8639, Japan
  • 14 Division of Molecular Pathology, Institute of Medical Science, the University of Tokyo, Tokyo, 108-8639, Japan
  • 15 Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan. yokada@sg.med.osaksa-u.ac.jp
Nat Commun, 2020 03 26;11(1):1569.
PMID: 32218440 DOI: 10.1038/s41467-020-15194-z

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.