{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sakaue S"],"funding":["Takeda Science Foundation","EPA","Japan Agency for Medical Research and Development","Ministry of Education, Culture, Sports, Science and Technology","Qatar National Research Fund"],"pagination":["1569"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7099015"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_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 deconvolu"],"journal":["Nature communications"],"pubmed_title":["Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction."],"pmcid":["PMC7099015"],"funding_grant_id":["15H05911, 19H01021","19gm6010001h0004, 19ek0410041h0003, 19ek0109413h0001, 19km0405211h0001","EP-C-17-017","4-344-3-105"],"pubmed_authors":["Kamatani Y","Hammoudeh M","Al Emadi S","Murakami Y","Sakaue S","Saxena R","Padyukov L","Suzuki K","Lai Too C","Uthman IW","Hirata M","Arayssi T","Okada Y","Kanai M","Akiyama M","Masri BK","Halabi H","Badsha H","Matsuda K","Hirata J"],"additional_accession":[]},"is_claimable":false,"name":"Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.","description":"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 deconvolu","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Mar","modification":"2026-06-04T02:17:19.586Z","creation":"2020-05-22T14:43:26Z"},"accession":"S-EPMC7099015","cross_references":{"pubmed":["32218440"],"doi":["10.1038/s41467-020-15194-z"]}}