• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

韩国基因组计划:1094 份具有临床信息的韩国人个人基因组。

Korean Genome Project: 1094 Korean personal genomes with clinical information.

机构信息

Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

Department of Biomedical Engineering, School of Life Sciences, UNIST, Ulsan 44919, Republic of Korea.

出版信息

Sci Adv. 2020 May 27;6(22):eaaz7835. doi: 10.1126/sciadv.aaz7835. eCollection 2020 May.

DOI:10.1126/sciadv.aaz7835
PMID:32766443
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7385432/
Abstract

We present the initial phase of the Korean Genome Project (Korea1K), including 1094 whole genomes (sequenced at an average depth of 31×), along with data of 79 quantitative clinical traits. We identified 39 million single-nucleotide variants and indels of which half were singleton or doubleton and detected Korean-specific patterns based on several types of genomic variations. A genome-wide association study illustrated the power of whole-genome sequences for analyzing clinical traits, identifying nine more significant candidate alleles than previously reported from the same linkage disequilibrium blocks. Also, Korea1K, as a reference, showed better imputation accuracy for Koreans than the 1KGP panel. As proof of utility, germline variants in cancer samples could be filtered out more effectively when the Korea1K variome was used as a panel of normals compared to non-Korean variome sets. Overall, this study shows that Korea1K can be a useful genotypic and phenotypic resource for clinical and ethnogenetic studies.

摘要

我们呈现了韩国基因组计划(Korea1K)的初始阶段,包括 1094 个全基因组(平均测序深度为 31×),以及 79 项定量临床特征数据。我们鉴定了 3900 万个单核苷酸变异和插入缺失,其中一半是单倍体或双倍体,并基于几种类型的基因组变异检测到了韩国特有的模式。全基因组关联研究说明了全基因组序列在分析临床特征方面的强大功能,鉴定出了比以前在相同连锁不平衡块中报道的 9 个更显著的候选等位基因。此外,Korea1K 作为参考,对韩国人的基因分型准确性优于 1KGP 面板。作为实用性的证明,与非韩国变体组相比,当将 Korea1K 变体组用作正常人群面板时,癌症样本中的种系变体可以更有效地被过滤掉。总体而言,这项研究表明 Korea1K 可以成为临床和种族研究的有用的基因型和表型资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/e95108ebe600/aaz7835-F5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/0b888903efce/aaz7835-F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/bcdc3260b36a/aaz7835-F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/7e09fc97ec5b/aaz7835-F3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/13932ad70035/aaz7835-F4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/e95108ebe600/aaz7835-F5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/0b888903efce/aaz7835-F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/bcdc3260b36a/aaz7835-F2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/7e09fc97ec5b/aaz7835-F3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/13932ad70035/aaz7835-F4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1593/7385432/e95108ebe600/aaz7835-F5.jpg

相似文献

1
Korean Genome Project: 1094 Korean personal genomes with clinical information.韩国基因组计划:1094 份具有临床信息的韩国人个人基因组。
Sci Adv. 2020 May 27;6(22):eaaz7835. doi: 10.1126/sciadv.aaz7835. eCollection 2020 May.
2
Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups.韩国 4K 计划:107 种表型的 4157 名韩国人全基因组序列源于广泛的健康检查。
Gigascience. 2024 Jan 2;13. doi: 10.1093/gigascience/giae014.
3
KoVariome: Korean National Standard Reference Variome database of whole genomes with comprehensive SNV, indel, CNV, and SV analyses.KoVariome:韩国全基因组标准参考变异组数据库,包含全面的单核苷酸变异、插入缺失、拷贝数变异和结构变异分析。
Sci Rep. 2018 Apr 4;8(1):5677. doi: 10.1038/s41598-018-23837-x.
4
NARD: whole-genome reference panel of 1779 Northeast Asians improves imputation accuracy of rare and low-frequency variants.NARD:1779 名东北亚人的全基因组参考面板提高了罕见和低频变异体的推断准确性。
Genome Med. 2019 Oct 22;11(1):64. doi: 10.1186/s13073-019-0677-z.
5
Regional genetic differences among Japanese populations and performance of genotype imputation using whole-genome reference panel of the Tohoku Medical Megabank Project.日本人群的区域遗传差异及使用东北医疗大数据项目全基因组参考面板进行基因型推断的性能。
BMC Genomics. 2018 Jul 24;19(1):551. doi: 10.1186/s12864-018-4942-0.
6
An ethnically relevant consensus Korean reference genome is a step towards personal reference genomes.一个具有种族相关性的韩国共识参考基因组是迈向个人参考基因组的一步。
Nat Commun. 2016 Nov 24;7:13637. doi: 10.1038/ncomms13637.
7
Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population.使用元填充分析韩国参考基因组可提高韩国人群中罕见变异的填充准确性和范围。
Front Genet. 2022 Nov 24;13:1008646. doi: 10.3389/fgene.2022.1008646. eCollection 2022.
8
KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing.KRGDB:基于全基因组测序的 1722 名韩国人大规模变异数据库。
Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baz146.
9
Inclusion of Population-specific Reference Panel from India to the 1000 Genomes Phase 3 Panel Improves Imputation Accuracy.纳入来自印度的特定人群参考面板可提高 1000 基因组计划第 3 阶段面板的推断准确性。
Sci Rep. 2017 Jul 27;7(1):6733. doi: 10.1038/s41598-017-06905-6.
10
Deep whole-genome sequencing of 90 Han Chinese genomes.对 90 个汉族个体的全基因组深度测序。
Gigascience. 2017 Sep 1;6(9):1-7. doi: 10.1093/gigascience/gix067.

引用本文的文献

1
Dynamic and reversible transcriptomic age shifts induced by COVID-19 in Korean whole blood.新冠病毒感染在韩国全血中引发的动态且可逆的转录组年龄变化
Aging (Albany NY). 2025 Jun 10;17(6):1484-1510. doi: 10.18632/aging.206270.
2
Estimating the Prevalence of Autosomal Recessive Neuromuscular Diseases in the Korean Population.估算韩国人群常染色体隐性神经肌肉疾病的患病率。
J Korean Med Sci. 2025 May 19;40(19):e68. doi: 10.3346/jkms.2025.40.e68.
3
Genetic and training adaptations in the Haenyeo divers of Jeju, Korea.韩国济州岛海女潜水员的基因与训练适应性

本文引用的文献

1
Assembly of a pan-genome from deep sequencing of 910 humans of African descent.从非洲裔 910 人的深度测序中组装泛基因组。
Nat Genet. 2019 Jan;51(1):30-35. doi: 10.1038/s41588-018-0273-y. Epub 2018 Nov 19.
2
COSMIC: the Catalogue Of Somatic Mutations In Cancer.COSMIC:癌症体细胞突变目录。
Nucleic Acids Res. 2019 Jan 8;47(D1):D941-D947. doi: 10.1093/nar/gky1015.
3
The UK Biobank resource with deep phenotyping and genomic data.英国生物银行资源库,具有深度表型和基因组数据。
Cell Rep. 2025 May 27;44(5):115577. doi: 10.1016/j.celrep.2025.115577. Epub 2025 May 2.
4
Genetic inactivation of FAAP100 causes Fanconi anemia due to disruption of the monoubiquitin ligase core complex.FAAP100的基因失活由于单泛素连接酶核心复合物的破坏而导致范可尼贫血。
J Clin Invest. 2025 Apr 15;135(11). doi: 10.1172/JCI187323. eCollection 2025 Jun 2.
5
Associations of polygenic risk score, environmental factors, and their interactions with the risk of schizophrenia spectrum disorders.多基因风险评分、环境因素及其相互作用与精神分裂症谱系障碍风险的关联。
Psychol Med. 2025 Apr 11;55:e111. doi: 10.1017/S0033291725000753.
6
Lessons from national biobank projects utilizing whole-genome sequencing for population-scale genomics.利用全基因组测序开展人群规模基因组学研究的国家生物样本库项目经验教训。
Genomics Inform. 2025 Mar 6;23(1):8. doi: 10.1186/s44342-025-00040-9.
7
Identification of a novel non-coding deletion in Allan-Herndon-Dudley syndrome by long-read HiFi genome sequencing.通过长读长HiFi基因组测序鉴定艾伦-赫恩登-达德利综合征中的一种新型非编码缺失。
BMC Med Genomics. 2025 Mar 3;18(1):41. doi: 10.1186/s12920-024-02058-4.
8
Understanding the genetic epidemiology of hereditary breast cancer in India using whole genome data from 1029 healthy individuals.利用1029名健康个体的全基因组数据了解印度遗传性乳腺癌的遗传流行病学。
Cancer Causes Control. 2025 Jul;36(7):673-682. doi: 10.1007/s10552-025-01974-9. Epub 2025 Mar 1.
9
Digenic impairments of haploinsufficient genes in patients with craniosynostosis.颅缝早闭患者中半合子不足基因的双基因损伤。
JCI Insight. 2025 Feb 24;10(4):e176985. doi: 10.1172/jci.insight.176985.
10
Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation.通过差异甲基化分析和基于机器学习的验证,鉴定出17种与焦虑症相关的新型表观遗传生物标志物。
Clin Epigenetics. 2025 Feb 17;17(1):24. doi: 10.1186/s13148-025-01819-x.
Nature. 2018 Oct;562(7726):203-209. doi: 10.1038/s41586-018-0579-z. Epub 2018 Oct 10.
4
Detecting Somatic Mutations in Normal Cells.检测正常细胞中的体细胞突变。
Trends Genet. 2018 Jul;34(7):545-557. doi: 10.1016/j.tig.2018.04.003. Epub 2018 May 3.
5
Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese.深度全基因组测序揭示了与日本人进化和疾病风险相关的近期选择特征。
Nat Commun. 2018 Apr 24;9(1):1631. doi: 10.1038/s41467-018-03274-0.
6
Genetic structure, divergence and admixture of Han Chinese, Japanese and Korean populations.汉族、日本人和韩国人群的遗传结构、分化及混合情况。
Hereditas. 2018 Apr 6;155:19. doi: 10.1186/s41065-018-0057-5. eCollection 2018.
7
KoVariome: Korean National Standard Reference Variome database of whole genomes with comprehensive SNV, indel, CNV, and SV analyses.KoVariome:韩国全基因组标准参考变异组数据库,包含全面的单核苷酸变异、插入缺失、拷贝数变异和结构变异分析。
Sci Rep. 2018 Apr 4;8(1):5677. doi: 10.1038/s41598-018-23837-x.
8
Ensembl 2018.Ensembl 2018.
Nucleic Acids Res. 2018 Jan 4;46(D1):D754-D761. doi: 10.1093/nar/gkx1098.
9
The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology.移动元件定位工具(MELT):大规模移动元件发现与生物学。
Genome Res. 2017 Nov;27(11):1916-1929. doi: 10.1101/gr.218032.116. Epub 2017 Aug 30.
10
Sequencing and de novo assembly of 150 genomes from Denmark as a population reference.丹麦 150 个个体基因组的测序和从头组装作为一个群体参考。
Nature. 2017 Aug 3;548(7665):87-91. doi: 10.1038/nature23264. Epub 2017 Jul 26.