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

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Sakaue S,Hirata J,Kanai M,Suzuki K,Akiyama M,Lai Too C,Arayssi T,Hammoudeh M,Al Emadi S,Masri BK,Halabi H,Badsha H,Uthman IW,Saxena R,Padyukov L,Hirata M,Matsuda K,Murakami Y,Kamatani Y,Okada Y

doi

10.1038/s41467-020-15194-z

subject

Has Abstract

pub_date

2020-03-26 00:00:00

pages

1569

issue

1

issn

2041-1723

pii

10.1038/s41467-020-15194-z

journal_volume

11

pub_type

杂志文章

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