1
|
Flanagan J, Liu X, Ortega-Reyes D, Tomizuka K, Matoba N, Akiyama M, Koido M, Ishigaki K, Ashikawa K, Takata S, Shi M, Aoi T, Momozawa Y, Ito K, Murakami Y, Matsuda K, Kamatani Y, Morris AP, Horikoshi M, Terao C. Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese population. Commun Biol 2024; 7:1665. [PMID: 39702642 DOI: 10.1038/s42003-024-07338-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 12/02/2024] [Indexed: 12/21/2024] Open
Abstract
To improve imputation quality for genome-wide association studies (GWAS) conducted on the Japanese population, we developed and evaluated four Japanese population-specific reference panels. These panels were constructed through the augmentation of the 1000 Genomes Project (1KG) panel using Japanese whole genome sequencing (WGS) data, with sample sizes ranging from 1 K to 7 K individuals enrolled through the Biobank Japan (BBJ) project, and sequencing depths ranging from 3× to 30×. Among these panels, an augmented reference panel comprising 7472 WGS samples of mixed depth (1KG+7K) exhibit the greatest improvement in imputation quality relative to the Trans-Omics for Precision Medicine (TOPMed) reference panel. Notably, we observe these improvements primarily for rare variants with a minor allele frequency (MAF) <5%. To demonstrate the benefits of improved imputation quality in association analyses of complex traits, we conducted GWAS for serum uric acid and total cholesterol levels following imputation up to the 1KG+7K panel. The analysis reveals several loci reaching genome-wide significance (P < 5 × 10-8) in the 1KG+7K imputation output yet remaining undetected when the same sample set is imputed up to the TOPMed reference panel. In summary, the 1KG+7K panel demonstrates significant advantages in the discovery of trait-associated loci, particularly those influenced by low-frequency association signals.
Collapse
Affiliation(s)
- Jack Flanagan
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea
| | - Xiaoxi Liu
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - David Ortega-Reyes
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for DNA Data Analysis, National Institute of Genetics, Shizuoka, Japan
- Department of Genetics, School of Life Science, The Graduate University for Advanced Studies, SOKENDAI, Kanagawa, Japan
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Nana Matoba
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Genetics, UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Masato Akiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kyota Ashikawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Sadaaki Takata
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - MingYang Shi
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Tomomi Aoi
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan.
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
| |
Collapse
|
2
|
Lee S, Shin D. A combination of red and processed meat intake and polygenic risk score influences the incidence of hyperuricemia in middle-aged Korean adults. Nutr Res Pract 2024; 18:721-745. [PMID: 39398885 PMCID: PMC11464275 DOI: 10.4162/nrp.2024.18.5.721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 08/03/2024] [Accepted: 08/22/2024] [Indexed: 10/15/2024] Open
Abstract
BACKGROUND/OBJECTIVES The high consumption of purine-rich meat is associated with hyperuricemia. However, there is limited evidence linking the consumption of red and processed meat to the genetic risk of hyperuricemia. We investigated the relationship between various combinations of red and processed meat consumption and the polygenic risk scores (PRSs) and the incidence of hyperuricemia in middle-aged Koreans. SUBJECTS/METHODS We analyzed the data from 44,053 participants aged ≥40 years sourced from the Health Examinees (HEXA) cohort of the Korean Genome and Epidemiology Study (KoGES). Information regarding red and processed meat intake was obtained using a semiquantitative food frequency questionnaire (SQ-FFQ). We identified 69 independent single-nucleotide polymorphisms (SNPs) at uric acid-related loci using genome-wide association studies (GWASs) and clumping analyses. The individual PRS, which is the weighted sum of the effect size of each allele at the SNP, was calculated. We used multivariable Cox proportional hazards models adjusted for covariates to determine the relationship between red and processed meat intake and the PRS in the incidence of hyperuricemia. RESULTS During an average follow-up period of 5 years, 2,556 patients with hyperuricemia were identified. For both men and women, the group with the highest red and processed meat intake and the highest PRS was positively associated with the development of hyperuricemia when compared with the group with the lowest red and processed meat intake and the lowest PRS (hazard ratio [HR], 2.72; 95% confidence interval [CI], 2.10-3.53; P < 0.0001; HR, 3.28; 95% CI, 2.45-4.40; P < 0.0001). CONCLUSION Individuals at a high genetic risk for uric acid levels should moderate their consumption of red and processed meat to prevent hyperuricemia.
Collapse
Affiliation(s)
- Suyeon Lee
- Department of Food and Nutrition, Inha University, Incheon 22212, Korea
| | - Dayeon Shin
- Department of Food and Nutrition, Inha University, Incheon 22212, Korea
| |
Collapse
|
3
|
Lee DJ, Moon JS, Song DK, Lee YS, Kim DS, Cho NJ, Gil HW, Lee EY, Park S. Genome-wide association study and fine-mapping on Korean biobank to discover renal trait-associated variants. Kidney Res Clin Pract 2024; 43:299-312. [PMID: 37919891 PMCID: PMC11181046 DOI: 10.23876/j.krcp.23.079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Chronic kidney disease is a significant health burden worldwide, with increasing incidence. Although several genome- wide association studies (GWAS) have investigated single nucleotide polymorphisms (SNP) associated with kidney trait, most studies were focused on European ancestry. METHODS We utilized clinical and genetic information collected from the Korean Genome and Epidemiology Study (KoGES). RESULTS More than five million SNPs from 58,406 participants were analyzed. After meta-GWAS, 1,360 loci associated with estimated glomerular filtration rate (eGFR) at a genome-wide significant level (p = 5 × 10-8) were identified. Among them, 399 loci were validated with at least one other biomarker (blood urea nitrogen [BUN] or eGFRcysC) and 149 loci were validated using both markers. Among them, 18 SNPs (nine known ones and nine novel ones) with 20 putative genes were found. The aggregated effect of genes estimated by MAGMA gene analysis showed that these significant genes were enriched in kidney-associated pathways, with the kidney and liver being the most enriched tissues. CONCLUSION In this study, we conducted GWAS for more than 50,000 Korean individuals and identified several variants associated with kidney traits, including eGFR, BUN, and eGFRcysC. We also investigated functions of relevant genes using computational methods to define putative causal variants.
Collapse
Affiliation(s)
- Dong-Jin Lee
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Jong-Seok Moon
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, Republic of Korea
| | - Dae Kwon Song
- Department of Biology, College of Natural Sciences, Soonchunhyang University, Asan, Republic of Korea
- Support Center (Core-Facility) for Bio-Bigdata Analysis and Utilization of Biological Resources, Soonchunhyang University, Asan, Republic of Korea
| | - Yong Seok Lee
- Department of Biology, College of Natural Sciences, Soonchunhyang University, Asan, Republic of Korea
- Support Center (Core-Facility) for Bio-Bigdata Analysis and Utilization of Biological Resources, Soonchunhyang University, Asan, Republic of Korea
| | - Dong-Sub Kim
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Nam-Jun Cho
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Hyo-Wook Gil
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Eun Young Lee
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Institute of Tissue Regeneration, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea
| | - Samel Park
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, Republic of Korea
| |
Collapse
|
4
|
Luo Y, Wu Q, Meng R, Lian F, Jiang C, Hu M, Wang Y, Ma H. Associations of serum uric acid with cardiovascular disease risk factors: a retrospective cohort study in southeastern China. BMJ Open 2023; 13:e073930. [PMID: 37758669 PMCID: PMC10537982 DOI: 10.1136/bmjopen-2023-073930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 08/18/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVE To evaluate the associations between serum uric acid (SUA) levels and cardiovascular disease (CVD) risk factors, focusing on potential sex-specific differences. DESIGN A retrospective cohort study. SETTING A large community-based survey was conducted every two years from 2010 to 2018 in Hangzhou, Zhejiang Province, outheastern China. PARTICIPANTS 6119 participants aged 40 years and above who underwent at least three times of physical examinations were enrolled. METHODS Participants were categorised into four groups (Q1-Q4) based on baseline SUA quartiles within the normal range, with hyperuricaemia (HUA) as the fifth group. The Q1 was the reference. By stratifying participants by gender, the relationships between SUA levels and systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG) and total cholesterol (TC) were investigated using linear regression models in the generalised estimating equation. Additionally, the associations of elevated SUA levels and HUA with hypertension, hyperglycaemia and dyslipidaemia were correspondingly examined using multivariate logistic regression models. RESULTS After adjusting for confounding variables, we found positive associations between SUA levels and SBP, DBP, FBG and TC in women, and with TC in men (p<0.01). Likewise, elevated SUA quartiles and HUA were linked to increased dyslipidaemia risk in both sexes, and increased hyperglycaemia risk only in women, with HRs (95% CI) of 1.64 (1.05 to 2.55) and 2.37 (1.47 to 3.81) in the Q4 and HUA group, respectively. Women with HUA had higher hypertension risk (HR=1.45, 95% CI 1.21 to 1.73), while no such association was observed in men. Stratified analyses revealed significant associations between elevated SUA levels and CVD risk factors in postmenopausal and non-obese women. CONCLUSIONS Elevated SUA levels increase the risk of dyslipidaemia in both sexes. SUA levels within normal range and HUA are positively associated with hyperglycaemia and hypertension in postmenopausal women, but not in men.
Collapse
Affiliation(s)
- Yingxian Luo
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Qiong Wu
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Runtang Meng
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Fuzhi Lian
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Chen Jiang
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Meiyu Hu
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Yaxin Wang
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Haiyan Ma
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| |
Collapse
|
5
|
Nam K, Kim J, Lee S. Genome-wide study on 72,298 individuals in Korean biobank data for 76 traits. CELL GENOMICS 2022; 2:100189. [PMID: 36777999 PMCID: PMC9903843 DOI: 10.1016/j.xgen.2022.100189] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/04/2022] [Accepted: 09/09/2022] [Indexed: 11/07/2022]
Abstract
Genome-wide association studies (GWAS) on diverse ancestry groups are lacking, resulting in deficits of genetic discoveries and polygenic scores. We conducted GWAS for 76 phenotypes in Korean biobank data, namely the Korean Genome and Epidemiology Study (KoGES) (n = 72,298). Our analysis discovered 2,242 associated loci, including 122 novel associations, many of which were replicated in Biobank Japan (BBJ) GWAS. We also applied several up-to-date methods for genetic association tests to increase the power, discovering additional associations that are not identified in simple case-control GWAS. We evaluated genetic pleiotropy to investigate genes associated with multiple traits. Following meta-analysis of 32 phenotypes between KoGES and BBJ, we further identified 379 novel associations and demonstrated the improved predictive performance of polygenic risk scores by using the meta-analysis results. The summary statistics of 76 KoGES GWAS phenotypes are publicly available, contributing to a better comprehension of the genetic architecture of the East Asian population.
Collapse
Affiliation(s)
- Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea
| | - Jangho Kim
- Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul 08826, Republic of Korea
| |
Collapse
|
6
|
Zhao J, Guo S, Schrodi SJ, He D. Trends in the Contribution of Genetic Susceptibility Loci to Hyperuricemia and Gout and Associated Novel Mechanisms. Front Cell Dev Biol 2022; 10:937855. [PMID: 35813212 PMCID: PMC9259951 DOI: 10.3389/fcell.2022.937855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Hyperuricemia and gout are complex diseases mediated by genetic, epigenetic, and environmental exposure interactions. The incidence and medical burden of gout, an inflammatory arthritis caused by hyperuricemia, increase every year, significantly increasing the disease burden. Genetic factors play an essential role in the development of hyperuricemia and gout. Currently, the search on disease-associated genetic variants through large-scale genome-wide scans has primarily improved our understanding of this disease. However, most genome-wide association studies (GWASs) still focus on the basic level, whereas the biological mechanisms underlying the association between genetic variants and the disease are still far from well understood. Therefore, we summarized the latest hyperuricemia- and gout-associated genetic loci identified in the Global Biobank Meta-analysis Initiative (GBMI) and elucidated the comprehensive potential molecular mechanisms underlying the effects of these gene variants in hyperuricemia and gout based on genetic perspectives, in terms of mechanisms affecting uric acid excretion and reabsorption, lipid metabolism, glucose metabolism, and nod-like receptor pyrin domain 3 (NLRP3) inflammasome and inflammatory pathways. Finally, we summarized the potential effect of genetic variants on disease prognosis and drug efficacy. In conclusion, we expect that this summary will increase our understanding of the pathogenesis of hyperuricemia and gout, provide a theoretical basis for the innovative development of new clinical treatment options, and enhance the capabilities of precision medicine for hyperuricemia and gout treatment.
Collapse
Affiliation(s)
- Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Shicheng Guo
- Computation and Informatics in Biology and Medicine, University of WI-Madison, Madison, WI, United States
- Department of Medical Genetics, School of Medicine and Public Health, University of WI-Madison, Madison, WI, United States
| | - Steven J. Schrodi
- Computation and Informatics in Biology and Medicine, University of WI-Madison, Madison, WI, United States
- Department of Medical Genetics, School of Medicine and Public Health, University of WI-Madison, Madison, WI, United States
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, Shanghai, China
- Arthritis Institute of Integrated Traditional and Western Medicine, Shanghai Chinese Medicine Research Institute, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|