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Yu S, Lin Y, Yang Y, Jin X, Liao B, Lu D, Huang J. Shared genetic effect of kidney function on bipolar and major depressive disorders: a large-scale genome-wide cross-trait analysis. Hum Genomics 2024; 18:60. [PMID: 38858783 PMCID: PMC11165782 DOI: 10.1186/s40246-024-00627-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND Epidemiological studies have revealed a significant association between impaired kidney function and certain mental disorders, particularly bipolar disorder (BIP) and major depressive disorder (MDD). However, the evidence regarding shared genetics and causality is limited due to residual confounding and reverse causation. METHODS In this study, we conducted a large-scale genome-wide cross-trait association study to investigate the genetic overlap between 5 kidney function biomarkers (eGFRcrea, eGFRcys, blood urea nitrogen (BUN), serum urate, and UACR) and 2 mental disorders (MDD, BIP). Summary-level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Psychiatric Genomics Consortium. RESULTS Using LD score regression, we found moderate but significant genetic correlations between kidney function biomarker traits on BIP and MDD. Cross-trait meta-analysis identified 1 to 19 independent significant loci that were found shared among 10 pairs of 5 kidney function biomarkers traits and 2 mental disorders. Among them, 3 novel genes: SUFU, IBSP, and PTPRJ, were also identified in transcriptome-wide association study analysis (TWAS), most of which were observed in the nervous and digestive systems (FDR < 0.05). Pathway analysis showed the immune system could play a role between kidney function biomarkers and mental disorders. Bidirectional mendelian randomization analysis suggested a potential causal relationship of kidney function biomarkers on BIP and MDD. CONCLUSIONS In conclusion, the study demonstrated that both BIP and MDD shared genetic architecture with kidney function biomarkers, providing new insights into their genetic architectures and suggesting that larger GWASs are warranted.
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Affiliation(s)
- Simin Yu
- Department of Urology, Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Yifei Lin
- Department of Urology, Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yong Yang
- Health Management Center, General Practice Medical Center, Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xi Jin
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Banghua Liao
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Donghao Lu
- Health Management Center, General Practice Medical Center, Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
- Institute of Environmental Medicine, Karolinska Institutet, Nobels Väg 13, 17177, Stockholm, Sweden.
| | - Jin Huang
- Department of Urology, Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
- Health Management Center, General Practice Medical Center, Innovation Institute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
- Health Management Center, General Practice Medical Center and Medical Device Regulatory Research and Evaluation Centre, West China Hospital, Sichuan University, Chengdu, People's Republic of China.
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2
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Huang L, Zhang J, Songyang Z, Xiong Y. Identification and Validation of eRNA as a Prognostic Indicator for Cervical Cancer. BIOLOGY 2024; 13:227. [PMID: 38666838 PMCID: PMC11048606 DOI: 10.3390/biology13040227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024]
Abstract
The survival of CESC patients is closely related to the expression of enhancer RNA (eRNA). In this work, we downloaded eRNA expression, clinical, and gene expression data from the TCeA and TCGA portals. A total of 7936 differentially expressed eRNAs were discovered by limma analysis, and the relationship between these eRNAs and survival was analyzed by univariate Cox hazard analysis, LASSO regression, and multivariate Cox hazard analysis to obtain an 8-eRNA model. Risk score heat maps, KM curves, ROC analysis, robustness analysis, and nomograms further indicate that this 8-eRNA model is a novel indicator with high prognostic performance independent of clinicopathological classification. The model divided patients into high-risk and low-risk groups, compared pathway diversity between the two groups through GSEA analysis, and provided potential therapeutic agents for high-risk patients.
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Affiliation(s)
- Lijing Huang
- MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; (L.H.); (J.Z.)
| | - Jingkai Zhang
- MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; (L.H.); (J.Z.)
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; (L.H.); (J.Z.)
- Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Yuanyan Xiong
- MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; (L.H.); (J.Z.)
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3
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Kim MJ, Jin HS, Eom YB. Coffee consumption affects kidney function based on GCKR polymorphism in a Korean population. Nutr Res 2024; 122:92-100. [PMID: 38215572 DOI: 10.1016/j.nutres.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/19/2023] [Accepted: 12/19/2023] [Indexed: 01/14/2024]
Abstract
Kidney function can be preserved through pharmacological interventions and nonpharmacological strategies, such as lifestyle and dietary adjustments. Among these, coffee has been linked to protective effects on kidney function. However, few studies have investigated the effect of coffee consumption on kidney function according to specific genes. We hypothesized that the impact of coffee consumption on kidney function might vary depending on GCKR polymorphism. GCKR rs1260326 polymorphism was examined using the Korean genome and epidemiology data from 656 chronic kidney disease (CKD) cases and 38,540 individuals without CKD (non-CKD). GCKR polymorphism has been previously associated with both coffee consumption and kidney function in Europeans. We replicated the associations between GCKR rs1260326 and coffee consumption and kidney function in Korean individuals. We also explored the effect of coffee consumption on kidney function by multivariate logistic regression analysis. Individuals with the rs1260326 (TC/CC) genotype did not experience significant changes in CKD risk based on their coffee consumption habits. In contrast, individuals with the TT genotype exhibited a significantly lower risk of CKD based on coffee consumption. Interestingly, in the non-CKD group, a beneficial effect on estimated glomerular filtration rate was observed in individuals with the T allele as coffee consumption increased. Our findings supported the hypothesis and revealed that the impact of coffee consumption habits on kidney function may vary based on the GCKR rs1260326 genotype of Korean individuals.
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Affiliation(s)
- Min-Jeong Kim
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Chungnam 31538, Republic of Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, Chungnam 31499, Republic of Korea
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Chungnam 31538, Republic of Korea; Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, Chungnam 31538, Republic of Korea.
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4
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Rashan EH, Bartlett AK, Khana DB, Zhang J, Jain R, Smith AJ, Baker ZN, Cook T, Caldwell A, Chevalier AR, Pfleger BF, Yuan P, Amador-Noguez D, Simcox JA, Pagliarini DJ. ACAD10 and ACAD11 enable mammalian 4-hydroxy acid lipid catabolism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574893. [PMID: 38260250 PMCID: PMC10802472 DOI: 10.1101/2024.01.09.574893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Fatty acid β-oxidation (FAO) is a central catabolic pathway with broad implications for organismal health. However, various fatty acids are largely incompatible with standard FAO machinery until they are modified by other enzymes. Included among these are the 4-hydroxy acids (4-HAs)-fatty acids hydroxylated at the 4 (γ) position-which can be provided from dietary intake, lipid peroxidation, and certain drugs of abuse. Here, we reveal that two atypical and poorly characterized acyl-CoA dehydrogenases (ACADs), ACAD10 and ACAD11, drive 4-HA catabolism in mice. Unlike other ACADs, ACAD10 and ACAD11 feature kinase domains N-terminal to their ACAD domains that phosphorylate the 4-OH position as a requisite step in the conversion of 4-hydroxyacyl-CoAs into 2-enoyl-CoAs-conventional FAO intermediates. Our ACAD11 cryo-EM structure and molecular modeling reveal a unique binding pocket capable of accommodating this phosphorylated intermediate. We further show that ACAD10 is mitochondrial and necessary for catabolizing shorter-chain 4-HAs, whereas ACAD11 is peroxisomal and enables longer-chain 4-HA catabolism. Mice lacking ACAD11 accumulate 4-HAs in their plasma while comparable 3- and 5-hydroxy acids remain unchanged. Collectively, this work defines ACAD10 and ACAD11 as the primary gatekeepers of mammalian 4-HA catabolism and sets the stage for broader investigations into the ramifications of aberrant 4-HA metabolism in human health and disease.
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Affiliation(s)
- Edrees H. Rashan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Abigail K. Bartlett
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Daven B. Khana
- Department of Microbiology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jingying Zhang
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Raghav Jain
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Andrew J. Smith
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Zakery N. Baker
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Taylor Cook
- Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706, USA
| | - Alana Caldwell
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Autumn R. Chevalier
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Brian F. Pfleger
- Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, WI 53706, USA
| | - Peng Yuan
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Daniel Amador-Noguez
- Department of Microbiology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Judith A. Simcox
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - David J. Pagliarini
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
- Morgridge Institute for Research, Madison, WI 53715, USA
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
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5
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Szrok-Jurga S, Czumaj A, Turyn J, Hebanowska A, Swierczynski J, Sledzinski T, Stelmanska E. The Physiological and Pathological Role of Acyl-CoA Oxidation. Int J Mol Sci 2023; 24:14857. [PMID: 37834305 PMCID: PMC10573383 DOI: 10.3390/ijms241914857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
Fatty acid metabolism, including β-oxidation (βOX), plays an important role in human physiology and pathology. βOX is an essential process in the energy metabolism of most human cells. Moreover, βOX is also the source of acetyl-CoA, the substrate for (a) ketone bodies synthesis, (b) cholesterol synthesis, (c) phase II detoxication, (d) protein acetylation, and (d) the synthesis of many other compounds, including N-acetylglutamate-an important regulator of urea synthesis. This review describes the current knowledge on the importance of the mitochondrial and peroxisomal βOX in various organs, including the liver, heart, kidney, lung, gastrointestinal tract, peripheral white blood cells, and other cells. In addition, the diseases associated with a disturbance of fatty acid oxidation (FAO) in the liver, heart, kidney, lung, alimentary tract, and other organs or cells are presented. Special attention was paid to abnormalities of FAO in cancer cells and the diseases caused by mutations in gene-encoding enzymes involved in FAO. Finally, issues related to α- and ω- fatty acid oxidation are discussed.
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Affiliation(s)
- Sylwia Szrok-Jurga
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Aleksandra Czumaj
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, 80-211 Gdansk, Poland;
| | - Jacek Turyn
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Areta Hebanowska
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
| | - Julian Swierczynski
- Institue of Nursing and Medical Rescue, State University of Applied Sciences in Koszalin, 75-582 Koszalin, Poland;
| | - Tomasz Sledzinski
- Department of Pharmaceutical Biochemistry, Faculty of Pharmacy, Medical University of Gdansk, 80-211 Gdansk, Poland;
| | - Ewa Stelmanska
- Department of Biochemistry, Faculty of Medicine, Medical University of Gdansk, 80-211 Gdansk, Poland; (S.S.-J.); (J.T.); (A.H.)
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6
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Zhang L, Zhang W, Xiao C, Wu X, Cui H, Yan P, Yang C, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Alfredsson L, Klareskog L, Yang Y, Yao Y, Li J, Liu Z, Yang C, Jiang X, Zhang B. Using human genetics to understand the epidemiological association between obesity, serum urate, and gout. Rheumatology (Oxford) 2023; 62:3280-3290. [PMID: 36734534 DOI: 10.1093/rheumatology/kead054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 12/31/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
OBJECTIVES We aimed to clarify the genetic overlaps underlying obesity-related traits, serum urate, and gout. METHODS We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci, and causal relationships between obesity (the exposure variable), gout (the primary outcome) and serum urate (the secondary outcome). Summary statistics were collected from the hitherto largest genome-wide association studies conducted for BMI (N = 806 834), waist-to-hip ratio (WHR; N = 697 734), WHR adjusted for BMI (WHRadjBMI; N = 694 649), serum urate (N = 288 649), and gout (Ncases = 13 179 and Ncontrols = 750 634). RESULTS Positive overall genetic correlations were observed for BMI (rg = 0.27, P = 6.62 × 10-7), WHR (rg = 0.22, P = 6.26 × 10-7) and WHRadjBMI (rg = 0.07, P = 6.08 × 10-3) with gout. Partitioning the whole genome into 1703 LD (linkage disequilibrium)-independent regions, a significant local signal at 4q22 was identified for BMI and gout. The global and local shared genetic basis was further strengthened by the multiple pleiotropic loci identified in the cross-phenotype association study, multiple shared gene-tissue pairs observed by Transcriptome-wide association studies, as well as causal relationships demonstrated by Mendelian randomization [BMI-gout: OR (odds ratio) = 1.66, 95% CI = 1.45, 1.88; WHR-gout: OR = 1.57, 95% CI = 1.37, 1.81]. Replacing the binary disease status of gout with its latent pathological measure, serum urate, a similar pattern of correlation, pleiotropy and causality was observed with even more pronounced magnitude and significance. CONCLUSION Our comprehensive genome-wide cross-trait analysis demonstrates a shared genetic basis and pleiotropic loci, as well as a causal relationship between obesity, serum urate, and gout, highlighting an intrinsic link underlying these complex traits.
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Affiliation(s)
- Li Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chenghan Xiao
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huijie Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peijing Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chao Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Mingshuang Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yunjie Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yanqiu Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lars Alfredsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine and Center for Molecular Medicine, Karolinska Institutet at Karolinska University Hospital (Solna), Stockholm, Sweden
| | - Yanfang Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuqin Yao
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Zhenmi Liu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chunxia Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xia Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ben Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Epidemiology and Biostatistics, West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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7
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Genetic Risk, Adherence to a Healthy Lifestyle, and Hyperuricemia: The TCLSIH Cohort Study. Am J Med 2023; 136:476-483.e5. [PMID: 36708795 DOI: 10.1016/j.amjmed.2023.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/03/2023] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
BACKGROUND Genetic factors have been associated with hyperuricemia in large studies, but the extent to which this can be offset by a healthy lifestyle is unknown. This study aimed to examine whether healthy lifestyle could reduce hyperuricemia risk among individuals with different genetic profiles. METHODS We defined a lifestyle score using body mass index, smoking, alcohol consumption, physical activities, and diets in 2796 unrelated individuals from the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort study. Polygenic risk scores (PRS) were constructed based on uric acid loci. Associations of combined lifestyle factors and genetic risk and incident hyperuricemia were estimated using Cox proportional hazard regression. RESULTS Of 2796 individuals, 747 participants (26.7%) developed hyperuricemia. Genetic risk and lifestyle were predictors of incident events, and they showed an interaction for the outcome. Compared with high PRS, low PRS reduced risk of incident hyperuricemia by 40%, and compared with unhealthy lifestyle, healthy lifestyle reduced risk of incident hyperuricemia by 41%. Compared with unhealthy lifestyle and high genetic risk, adherence to healthy lifestyle was associated with a 68% (95% confidence interval, 44%-81%) lower risk of hyperuricemia among those at a low genetic risk. CONCLUSIONS In this prospective cohort study, we observed an interaction between genetics and lifestyle and the risk of hyperuricemia. The public health implication is that a healthy lifestyle is important for hyperuricemia prevention, especially for individuals with high genetic risk scores.
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8
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Yin X, Chan LS, Bose D, Jackson AU, VandeHaar P, Locke AE, Fuchsberger C, Stringham HM, Welch R, Yu K, Fernandes Silva L, Service SK, Zhang D, Hector EC, Young E, Ganel L, Das I, Abel H, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Wagner GR, Kang J, Morrison J, Burant CF, Collins FS, Ripatti S, Palotie A, Freimer NB, Mohlke KL, Scott LJ, Wen X, Fauman EB, Laakso M, Boehnke M. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun 2022; 13:1644. [PMID: 35347128 PMCID: PMC8960770 DOI: 10.1038/s41467-022-29143-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/23/2022] [Indexed: 01/13/2023] Open
Abstract
Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.
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Affiliation(s)
- Xianyong Yin
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Lap Sum Chan
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Debraj Bose
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Anne U. Jackson
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Peter VandeHaar
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Adam E. Locke
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Christian Fuchsberger
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA ,grid.511439.bInstitute for Biomedicine, Eurac Research, Bolzano, 39100 Italy
| | - Heather M. Stringham
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Ryan Welch
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Ketian Yu
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Lilian Fernandes Silva
- grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210 Finland
| | - Susan K. Service
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024 USA
| | - Daiwei Zhang
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA ,grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
| | - Emily C. Hector
- grid.40803.3f0000 0001 2173 6074Department of Statistics, North Carolina State University, Raleigh, NC 27695 USA
| | - Erica Young
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110 USA
| | - Liron Ganel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Indraniel Das
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA
| | - Haley Abel
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - Michael R. Erdos
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Lori L. Bonnycastle
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Johanna Kuusisto
- grid.9668.10000 0001 0726 2490Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210 Finland ,grid.410705.70000 0004 0628 207XCenter for Medicine and Clinical Research, Kuopio University Hospital, Kuopio, 70210 Finland
| | - Nathan O. Stitziel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108 USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110 USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St Louis, MO 63110 USA
| | - Ira M. Hall
- grid.47100.320000000419368710Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510 USA
| | - Gregory R. Wagner
- grid.429438.00000 0004 0402 1933Metabolon, Inc., Morrisville, NC 27560 USA
| | | | - Jian Kang
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Jean Morrison
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Charles F. Burant
- grid.214458.e0000000086837370Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109 USA
| | - Francis S. Collins
- grid.94365.3d0000 0001 2297 5165Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892 USA
| | - Samuli Ripatti
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290 Finland ,grid.7737.40000 0004 0410 2071Department of Public Health, University of Helsinki, Helsinki, 00014 Finland ,grid.66859.340000 0004 0546 1623Broad Institute of MIT & Harvard, Cambridge, MA 02142 USA
| | - Aarno Palotie
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, 00290 Finland ,grid.7737.40000 0004 0410 2071Department of Public Health, University of Helsinki, Helsinki, 00014 Finland ,grid.32224.350000 0004 0386 9924Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114 USA
| | - Nelson B. Freimer
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024 USA
| | - Karen L. Mohlke
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Laura J. Scott
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Xiaoquan Wen
- grid.214458.e0000000086837370Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109 USA
| | - Eric B. Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139 USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland.
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA.
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9
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Polimanti R, Walters RK, Johnson EC, McClintick JN, Adkins AE, Adkins DE, Bacanu SA, Bierut LJ, Bigdeli TB, Brown S, Bucholz KK, Copeland WE, Costello EJ, Degenhardt L, Farrer LA, Foroud TM, Fox L, Goate AM, Grucza R, Hack LM, Hancock DB, Hartz SM, Heath AC, Hewitt JK, Hopfer CJ, Johnson EO, Kendler KS, Kranzler HR, Krauter K, Lai D, Madden PAF, Martin NG, Maes HH, Nelson EC, Peterson RE, Porjesz B, Riley BP, Saccone N, Stallings M, Wall TL, Webb BT, Wetherill L, Edenberg HJ, Agrawal A, Gelernter J. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol Psychiatry 2020; 25:1673-1687. [PMID: 32099098 PMCID: PMC7392789 DOI: 10.1038/s41380-020-0677-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/15/2020] [Accepted: 01/30/2020] [Indexed: 01/17/2023]
Abstract
To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
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Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jeanette N McClintick
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Amy E Adkins
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Daniel E Adkins
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Silviu-Alin Bacanu
- Virginia Commonwealth University Alcohol Research Center, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Sandra Brown
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - William E Copeland
- Department of Psychiatry, University of Vermont Medical Center, Burlington, VT, USA
| | - E Jane Costello
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Louisa Degenhardt
- National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, USA
| | - Tatiana M Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Louis Fox
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Richard Grucza
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Laura M Hack
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Dana B Hancock
- Center for Omics Discovery and Epidemiology, RTI International, Research Triangle Park, NC, USA
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Christian J Hopfer
- Department of Psychiatry, University of Colorado Denver, Aurora, CO, USA
| | - Eric O Johnson
- Center for Omics Discovery and Epidemiology, RTI International, Research Triangle Park, NC, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Henry R Kranzler
- Center for Studies of Addiction, University of Pennsylvania Perelman School of Medicine, VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Kenneth Krauter
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Hermine H Maes
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Elliot C Nelson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Brien P Riley
- Virginia Commonwealth University Alcohol Research Center, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Nancy Saccone
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
| | - Michael Stallings
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Tamara L Wall
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Bradley T Webb
- Virginia Commonwealth University Alcohol Research Center, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA.
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10
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Cho SK, Kim B, Myung W, Chang Y, Ryu S, Kim HN, Kim HL, Kuo PH, Winkler CA, Won HH. Polygenic analysis of the effect of common and low-frequency genetic variants on serum uric acid levels in Korean individuals. Sci Rep 2020; 10:9179. [PMID: 32514006 PMCID: PMC7280503 DOI: 10.1038/s41598-020-66064-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 05/05/2020] [Indexed: 01/28/2023] Open
Abstract
Increased serum uric acid (SUA) levels cause gout and are associated with multiple diseases, including chronic kidney disease. Previous genome-wide association studies (GWAS) have identified more than 180 loci that contribute to SUA levels. Here, we investigated genetic determinants of SUA level in the Korean population. We conducted a GWAS for SUA in 6,881 Korean individuals, calculated polygenic risk scores (PRSs) for common variants, and validated the association of low-frequency variants and PRS with SUA levels in 3,194 individuals. We identified two low-frequency and six common independent variants associated with SUA. Despite the overall similar effect sizes of variants in Korean and European populations, the proportion of variance for SUA levels explained by the variants was greater in the Korean population. A rare, nonsense variant SLC22A12 p.W258X showed the most significant association with reduced SUA levels, and PRSs of common variants associated with SUA levels were significant in multiple Korean cohorts. Interestingly, an East Asian-specific missense variant (rs671) in ALDH2 displayed a significant association on chromosome 12 with the SUA level. Further genetic epidemiological studies on SUA are needed in ethnically diverse cohorts to investigate rare or low-frequency variants and determine the influence of genetic and environmental factors on SUA.
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Affiliation(s)
- Sung Kweon Cho
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.,Molecular Genetic Epidemiology Section, Basic Research Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Beomsu Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seungho Ryu
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Han-Na Kim
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyung-Lae Kim
- Department of Biochemistry, Ewha Womans University, Seoul, Republic of Korea
| | - Po-Hsiu Kuo
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Cheryl A Winkler
- Molecular Genetic Epidemiology Section, Basic Research Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.
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11
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Nakayama A, Nakatochi M, Kawamura Y, Yamamoto K, Nakaoka H, Shimizu S, Higashino T, Koyama T, Hishida A, Kuriki K, Watanabe M, Shimizu T, Ooyama K, Ooyama H, Nagase M, Hidaka Y, Matsui D, Tamura T, Nishiyama T, Shimanoe C, Katsuura-Kamano S, Takashima N, Shirai Y, Kawaguchi M, Takao M, Sugiyama R, Takada Y, Nakamura T, Nakashima H, Tsunoda M, Danjoh I, Hozawa A, Hosomichi K, Toyoda Y, Kubota Y, Takada T, Suzuki H, Stiburkova B, Major TJ, Merriman TR, Kuriyama N, Mikami H, Takezaki T, Matsuo K, Suzuki S, Hosoya T, Kamatani Y, Kubo M, Ichida K, Wakai K, Inoue I, Okada Y, Shinomiya N, Matsuo H. Subtype-specific gout susceptibility loci and enrichment of selection pressure on ABCG2 and ALDH2 identified by subtype genome-wide meta-analyses of clinically defined gout patients. Ann Rheum Dis 2020; 79:657-665. [PMID: 32238385 PMCID: PMC7213308 DOI: 10.1136/annrheumdis-2019-216644] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/01/2023]
Abstract
Objectives Genome-wide meta-analyses of clinically defined gout were performed to identify subtype-specific susceptibility loci. Evaluation using selection pressure analysis with these loci was also conducted to investigate genetic risks characteristic of the Japanese population over the last 2000–3000 years. Methods Two genome-wide association studies (GWASs) of 3053 clinically defined gout cases and 4554 controls from Japanese males were performed using the Japonica Array and Illumina Array platforms. About 7.2 million single-nucleotide polymorphisms were meta-analysed after imputation. Patients were then divided into four clinical subtypes (the renal underexcretion type, renal overload type, combined type and normal type), and meta-analyses were conducted in the same manner. Selection pressure analyses using singleton density score were also performed on each subtype. Results In addition to the eight loci we reported previously, two novel loci, PIBF1 and ACSM2B, were identified at a genome-wide significance level (p<5.0×10–8) from a GWAS meta-analysis of all gout patients, and other two novel intergenic loci, CD2-PTGFRN and SLC28A3-NTRK2, from normal type gout patients. Subtype-dependent patterns of Manhattan plots were observed with subtype GWASs of gout patients, indicating that these subtype-specific loci suggest differences in pathophysiology along patients’ gout subtypes. Selection pressure analysis revealed significant enrichment of selection pressure on ABCG2 in addition to ALDH2 loci for all subtypes except for normal type gout. Conclusions Our findings on subtype GWAS meta-analyses and selection pressure analysis of gout will assist elucidation of the subtype-dependent molecular targets and evolutionary involvement among genotype, phenotype and subtype-specific tailor-made medicine/prevention of gout and hyperuricaemia.
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Affiliation(s)
- Akiyoshi Nakayama
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.,Medical Squadron, Air Base Group, Western Aircraft Control and Warning Wing, Japan Air Self-Defense Force, Kasuga, Japan
| | - Masahiro Nakatochi
- Division of Department of Nursing, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yusuke Kawamura
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.,Department of General Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Hirofumi Nakaoka
- Division of Human Genetics, Department of Integrated Genetics, National Institute of Genetics, Mishima, Japan
| | - Seiko Shimizu
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Toshihide Higashino
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.,Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Teruhide Koyama
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, School of Food and Nutritional Sciences, University of Shizuoka, Shizuoka, Japan
| | - Miki Watanabe
- Department of Public Health, Nagoya City University Graduate School Medical Science, Nagoya, Japan
| | - Toru Shimizu
- Midorigaoka Hospital, Takatsuki, Japan.,Kyoto Industrial Health Association, Kyoto, Japan
| | | | | | | | | | - Daisuke Matsui
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takeshi Nishiyama
- Department of Public Health, Nagoya City University Graduate School Medical Science, Nagoya, Japan
| | - Chisato Shimanoe
- Department of Preventive Medicine, Faculty of Medicine, Saga University, Saga, Japan.,Clinical Research Center, Saga University Hospital, Saga, Japan
| | - Sakurako Katsuura-Kamano
- Department of Preventive Medicine, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan
| | - Naoyuki Takashima
- Department of Health Science, Shiga University of Medical Science, Otsu, Japan.,Department of Public Health, Faculty of Medicine, Kindai University, Osaka-Sayama, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Makoto Kawaguchi
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.,Department of Urology, National Defense Medical College, Tokorozawa, Japan
| | - Mikiya Takao
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan.,Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | - Ryo Sugiyama
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Yuzo Takada
- Faculty of Medical Science, Teikyo University of Science, Tokyo, Japan
| | - Takahiro Nakamura
- Laboratory for Mathematics, National Defense Medical College, Tokorozawa, Japan
| | - Hiroshi Nakashima
- Department of Preventive Medicine and Public Health, National Defense Medical College, Tokorozawa, Japan
| | - Masashi Tsunoda
- Department of Preventive Medicine and Public Health, National Defense Medical College, Tokorozawa, Japan
| | - Inaho Danjoh
- Group of Privacy Controls, Tohoku Medical Megabank Organization, Sendai, Japan
| | - Atsushi Hozawa
- Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Kazuyoshi Hosomichi
- Department of Bioinformatics and Genomics, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Yu Toyoda
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo, Japan
| | - Yu Kubota
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo, Japan
| | - Tappei Takada
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Suzuki
- Department of Pharmacy, The University of Tokyo Hospital, Tokyo, Japan
| | - Blanka Stiburkova
- Department of Pediatrics and Adolescent Medicine, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.,Institute of Rheumatology, Prague, Czech Republic
| | - Tanya J Major
- Department of Biochemisty, University of Otago, Dunedin, New Zealand
| | - Tony R Merriman
- Department of Biochemisty, University of Otago, Dunedin, New Zealand
| | - Nagato Kuriyama
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Haruo Mikami
- Cancer Prevention Center, Chiba Cancer Center Research Institute, Chiba, Japan
| | - Toshiro Takezaki
- Department of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan.,Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sadao Suzuki
- Department of Public Health, Nagoya City University Graduate School Medical Science, Nagoya, Japan
| | - Tatsuo Hosoya
- Division of Kidney and Hypertension, Department of Internal Medicine, Jikei University School of Medicine, Tokyo, Japan.,Department of Pathophysiology and Therapy in Chronic Kidney Disease, Jikei University School of Medicine, Tokyo, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kimiyoshi Ichida
- Division of Kidney and Hypertension, Department of Internal Medicine, Jikei University School of Medicine, Tokyo, Japan.,Department of Pathophysiology, Tokyo University of Pharmacy and Life Science, Hachioji, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ituro Inoue
- Division of Human Genetics, Department of Integrated Genetics, National Institute of Genetics, Mishima, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Nariyoshi Shinomiya
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Tokorozawa, Japan
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Yasukochi Y, Sakuma J, Takeuchi I, Kato K, Oguri M, Fujimaki T, Horibe H, Yamada Y. Evolutionary history of disease-susceptibility loci identified in longitudinal exome-wide association studies. Mol Genet Genomic Med 2019; 7:e925. [PMID: 31402603 PMCID: PMC6732299 DOI: 10.1002/mgg3.925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 06/12/2019] [Accepted: 07/26/2019] [Indexed: 12/17/2022] Open
Abstract
Background Our longitudinal exome‐wide association studies previously detected various genetic determinants of complex disorders using ~26,000 single‐nucleotide polymorphisms (SNPs) that passed quality control and longitudinal medical examination data (mean follow‐up period, 5 years) in 4884–6022 Japanese subjects. We found that allele frequencies of several identified SNPs were remarkably different among four ethnic groups. Elucidating the evolutionary history of disease‐susceptibility loci may help us uncover the pathogenesis of the related complex disorders. Methods In the present study, we conducted evolutionary analyses such as extended haplotype homozygosity, focusing on genomic regions containing disease‐susceptibility loci and based on genotyping data of our previous studies and datasets from the 1000 Genomes Project. Results Our evolutionary analyses suggest that derived alleles of rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs closely located at 12q24.1 associated with type 2 diabetes mellitus, obesity, dyslipidemia, and three complex disorders (hypertension, hyperuricemia, and dyslipidemia), respectively, rapidly expanded after the human dispersion from Africa (Out‐of‐Africa). Allele frequencies of GGA3 and six SNPs at 12q24.1 appeared to have remarkably changed in East Asians, whereas the derived alleles of rs34902660 of SLC17A3 and rs7656604 at 4q13.3 might have spread across Japanese and non‐Africans, respectively, although we cannot completely exclude the possibility that allele frequencies of disease‐associated loci may be affected by demographic events. Conclusion Our findings indicate that derived allele frequencies of nine disease‐associated SNPs (rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs at 12q24.1) identified in the longitudinal exome‐wide association studies largely increased in non‐Africans after Out‐of‐Africa.
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Affiliation(s)
- Yoshiki Yasukochi
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Japan
| | - Jun Sakuma
- CREST, Japan Science and Technology Agency, Kawaguchi, Japan.,Computer Science Department, College of Information Science, University of Tsukuba, Tsukuba, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Ichiro Takeuchi
- CREST, Japan Science and Technology Agency, Kawaguchi, Japan.,RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.,Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan
| | - Kimihiko Kato
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,Department of Internal Medicine, Meitoh Hospital, Nagoya, Japan
| | - Mitsutoshi Oguri
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,Department of Cardiology, Kasugai Municipal Hospital, Kasugai, Japan
| | - Tetsuo Fujimaki
- Department of Cardiovascular Medicine, Inabe General Hospital, Inabe, Japan
| | - Hideki Horibe
- Department of Cardiovascular Medicine, Gifu Prefectural Tajimi Hospital, Tajimi, Japan
| | - Yoshiji Yamada
- Department of Human Functional Genomics, Advanced Science Research Promotion Center, Mie University, Tsu, Japan.,CREST, Japan Science and Technology Agency, Kawaguchi, Japan
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