Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

Author:

Jeon Sungwon12ORCID,Choi Hansol13ORCID,Jeon Yeonsu12ORCID,Choi Whan-Hyuk134ORCID,Choi Hyunjoo13ORCID,An Kyungwhan13ORCID,Ryu Hyojung12ORCID,Bhak Jihun13ORCID,Lee Hyeonjae13ORCID,Kwon Yoonsung13ORCID,Ha Sukyeon15ORCID,Kim Yeo Jin2ORCID,Blazyte Asta136ORCID,Kim Changjae2ORCID,Kim Yeonkyung2ORCID,Kang Younghui12ORCID,Woo Yeong Ju2ORCID,Lee Chanyoung13ORCID,Seo Jeongwoo13ORCID,Yoon Changhan13ORCID,Bolser Dan7ORCID,Biro Orsolya8ORCID,Shin Eun-Seok9ORCID,Kim Byung Chul2ORCID,Kim Seon-Young10ORCID,Park Ji-Hwan10ORCID,Jeon Jongbum10ORCID,Jung Dooyoung3ORCID,Lee Semin13ORCID,Bhak Jong12311ORCID

Affiliation:

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

2. Clinomics, Inc. , Ulsan 44919 , Republic of Korea

3. Department of Biomedical Engineering, College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST) , Ulsan 44919 , Republic of Korea

4. Department of Mathematics, Kangwon National University , Chuncheon 24341 , Republic of Korea

5. Department of Computer Science & Engineering (CSE), College of Information-Bio Convergence Engineering, Ulsan National Institute of Science and Technology (UNIST) , Ulsan 44919 , Republic of Korea

6. Lee Gil Ya Cancer and Diabetes Institute, Gachon University , Incheon 21999 , Republic of Korea

7. Geromics Ltd. , Cambridge CB1 3NF , United Kingdom

8. Clinomics Europe Ltd. , Budapest 1094 , Hungary

9. Department of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine , Ulsan 44033 , Republic of Korea

10. Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology , Daejeon 34141 , Republic of Korea

11. Personal Genomics Institute (PGI) , Genome Research Foundation (GRF), Osong 28160 , Republic of Korea

Abstract

Abstract Background Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole genome–wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole genome and matched phenome data to conduct further population-genome studies and develop health care services based on population genomics. Results Here, we present 4,157 whole genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency >0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new genotype–phenotype associations that were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories. Conclusions Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome–phenome associations. The large-scale pathological whole genome–wide omics data will become a powerful set for genome–phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.

Funder

Korea Institute of Science and Technology Information

National Institutes of Health

Publisher

Oxford University Press (OUP)

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