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Research Publication

Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.

Liu Xiaoxi, X Koyama, Satoshi S et al.

38630824 PubMed ID
28 Authors
2024-04-19 Published
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

LX
Liu Xiaoxi
XK
X Koyama
SS
Satoshi S
TK
Tomizuka Kohei
KT
K Takata
SS
Sadaaki S
IY
Ishikawa Yuki
YI
Y Ito
SS
Shuji S
KS
Kosugi Shunichi
SS
S Suzuki
KK
Kunihiko K
HK
Hikino Keiko
KK
K Koido
MM
Masaru M
KY
Koike Yoshinao
YH
Y Horikoshi
MM
Momoko M
GT
Gakuhari Takashi
TI
T Ikegawa
SS
Shiro S
MK
Matsuda Kochi
KM
K Momozawa
YY
Yukihide Y
IK
Ito Kaoru
KK
K Kamatani
YY
Yoichiro Y
TC
Terao Chikashi
Chapter II

Abstract

Summary of the research findings

We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.

Chapter III

Analysis

Comprehensive review of ancestry and genetic findings

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Summary

Key Findings

Ancestry Insights

Traits Analysis

Historical Context

Scientific Assessment