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, Hussein Halabi, Humeira Badsha, Imad W. Uthman, Richa Saxena, Leonid Padyukov, Makoto Hirata, Koichi Matsuda, Yoshinori Murakami, Yoichiro Kamatani and Yukinori Okada ()
Additional contact information
Saori Sakaue: Osaka University Graduate School of Medicine
Jun Hirata: Osaka University Graduate School of Medicine
Masahiro Kanai: Osaka University Graduate School of Medicine
Ken Suzuki: Osaka University Graduate School of Medicine
Masato Akiyama: RIKEN Center for Integrative Medical Sciences
Chun Lai Too: Ministry of Health Malaysia
Thurayya Arayssi: Weill Cornell Medicine-Qatar, Education City
Mohammed Hammoudeh: Hamad Medical Corporation
Samar Al Emadi: Hamad Medical Corporation
Basel K. Masri: Jordan Hospital
Hussein Halabi: King Faisal Specialist Hospital and Research Center
Humeira Badsha: Emirates Hospital
Imad W. Uthman: American University of Beirut
Richa Saxena: Harvard Medical School
Leonid Padyukov: Karolinska Institutet and Karolinska University Hospital
Makoto Hirata: the University of Tokyo
Koichi Matsuda: the University of Tokyo
Yoshinori Murakami: the University of Tokyo
Yoichiro Kamatani: RIKEN Center for Integrative Medical Sciences
Yukinori Okada: Osaka University Graduate School of Medicine

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-15194-z

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