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Liao WL, Huang YC, Chang YW, Cheng CF, Liu TY, Lu HF, Chen HL, Tsai FJ. Impact of polygenic risk score for triglyceride trajectory and diabetic complications in subjects with type 2 diabetes based on large electronic medical record data from Taiwan: a case control study. J Endocrinol Invest 2024; 47:3101-3110. [PMID: 38795312 DOI: 10.1007/s40618-024-02397-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/15/2024] [Indexed: 05/27/2024]
Abstract
BACKGROUND The prevalence of diabetic dyslipidemia has gradually increased worldwide and individuals with hypertriglyceridemia often have a high polygenic burden of triglyceride (TG)-increasing variants. However, the contribution of genetic variants to dyslipidemia in patients with type 2 diabetes (T2D) remains limited. Therefore, in this study, we aimed to investigate the genetic characteristics of longitudinal changes in TG levels among patients with T2D and summarize the genetic effects of polygenic risk score (PRS) on TG trajectory and risk of diabetic complications. METHODS We conducted a case-control study. A total of 11,312 patients with T2D with longitudinal TG and genetic data were identified from a large hospital database in Taiwan. We then performed a genome-wide association study and calculated the relative PRS. RESULTS In total, 21 single-nucleotide polymorphisms (SNPs) related to TG trajectory were identified and yielded an area under the receiver operating characteristic curve (ROC) of 0.712 for high TG trajectory risk among Taiwanese patients with T2D. A cumulative genetic effect was observed for high TG trajectory, even when considering the adherence of a lipid-lowering agent in stratified analysis. An increased PRS increases high TG trajectory risk in a logistic regression model (odds ratio = 1.55; 95% confidence interval [CI] = 1.31-1.83 in the validation cohort). The TG-specific PRS was associated with the risk of diabetic microvascular complications, including diabetic retinopathy and nephropathy (with hazard ratios of 1.11 [95% CI = 1.01-1.21, P = 0.027] and 1.05 [95% CI = 1.01-1.1, P = 0.018], respectively). CONCLUSIONS This study may contribute to the identification of patients with T2D who are at risk of abnormal TG levels and diabetic microvascular complications using polygenic information.
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Affiliation(s)
- W-L Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Y-C Huang
- School of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Y-W Chang
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan
| | - C-F Cheng
- Big Data Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - T-Y Liu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan
| | - H-F Lu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan
| | - H-L Chen
- Big Data Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - F-J Tsai
- School of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan.
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.
- Division of Medical Genetics, China Medical University Children's Hospital, Taichung, 40447, Taiwan.
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, 413305, Taiwan.
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Lin TC, Huang CY, Li YL, Chiou HY, Hu CJ, Jeng JS, Tang SC, Chan L, Lien LM, Lin HJ, Lin CC, Hsieh YC. Association between high-density lipoprotein and functional outcome of ischemic stroke patients in a Taiwanese population. Lipids Health Dis 2024; 23:275. [PMID: 39210350 PMCID: PMC11363607 DOI: 10.1186/s12944-024-02265-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
Despite recent findings indicating a paradoxical association between high-density lipoprotein cholesterol (HDL-C) levels and cardiovascular disease (CVD) mortality, the impact of HDL-C on subsequent outcomes after ischemic stroke remains unclear. The study aims to investigate the relationships between HDL-C levels and post-stroke functional outcomes while examining the potential modifying influence of HDL-C-related single nucleotide polymorphisms identified through genome-wide association studies. This cohort study included 1,310 patients diagnosed with acute ischemic stroke (AIS), all of whom had their admission serum lipid profile and genotyping information. Participants were categorized into four groups based on gender and HDL-C level. Prognostic outcomes were assessed using a modified Rankin Scale (mRS) at 1, 3, and 12 months post-admission. Multivariate logistic regression and restricted cubic spline regression analysis were used to assess the associations between HDL-C levels and outcomes. The mean age of patients was 61.17 ± 12.08 years, and 69.31% were men. After adjusting confounders, patients with the highest HDL-C level group had a significantly higher risk of poor functional outcomes at 1, 3, and 12 months following stroke compared to the reference group. Restricted cubic splines depicted a nonlinear association between HDL-C levels and poor prognosis in both men and women. The ABCA1 gene rs2575876 AA genotype combined with abnormal HDL-C levels exhibited a significantly heightened risk of post-stroke adverse outcomes at 1 and 3 months compared to patients with normal HDL-C levels and GG + GA genotype. These findings suggest that the combined effects of ABCA1 genetic variants with either low or high HDL-C levels could further heighten this risk.
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Affiliation(s)
- Ting-Chun Lin
- Department of Neurosurgery, Hokkaido University, Sapporo, Japan
| | - Chun-Yao Huang
- Division of Cardiology and Cardiovascular Research Center, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- Taipei Heart Institute, Taipei Medical University, Taipei, Taiwan
| | - Yu-Ling Li
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Hung-Yi Chiou
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Chaur-Jong Hu
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Jiann-Shing Jeng
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Sung-Chun Tang
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Lung Chan
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Li-Ming Lien
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
| | - Huey-Juan Lin
- Department of Neurology, Chi-Mei Medical Center, Tainan, 71004, Taiwan
| | - Chu-Chien Lin
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Chen Hsieh
- Ph.D Program of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei City, Taiwan.
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Lee M, Park T, Shin JY, Park M. A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data. Sci Rep 2024; 14:17851. [PMID: 39090161 PMCID: PMC11294629 DOI: 10.1038/s41598-024-68541-1] [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: 02/26/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.
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Affiliation(s)
- Minhyuk Lee
- Department of Statistics, Korea University, Seoul, Republic of Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Ji-Yeon Shin
- Department of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
| | - Mira Park
- Department of Preventive Medicine, School of Medicine, Eulji University, Daejeon, Republic of Korea.
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Malinowski D, Safranow K, Pawlik A. PON1 rs662, rs854560 and TRIB1 rs17321515, rs2954029 Gene Polymorphisms Are Associated with Lipid Parameters in Patients with Unstable Angina. Genes (Basel) 2024; 15:871. [PMID: 39062650 PMCID: PMC11275408 DOI: 10.3390/genes15070871] [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: 06/12/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
Acute coronary heart disease (CHD) is mainly caused by the rupture of an unstable atherosclerotic plaque. Many different factors can cause stenosis or even occlusion of the coronary artery lumen, such as vasculitis and platelet aggregation. Our study was performed to assess the association between PON1 rs662, rs854560 and TRIB1 rs17321515, rs2954029 polymorphisms and the risk of CHD, as well as the association between studied polymorphisms and selected clinical parameters affecting the risk of developing ischemic heart disease. A total of 232 patients with unstable angina were enrolled in this study. There were no statistically significant differences in the PON1 rs662, rs854560 and TRIB1 rs17321515, rs2954029 polymorphism distributions between the total study and control groups. Total cholesterol plasma levels were significantly higher in patients with the PON1 rs662 TT genotype compared to those with the CC+TC genotypes, as well as in patients with the PON1 rs854560 TT genotype compared to those with the AA+AT genotypes. LDL plasma levels were significantly increased in patients with the PON1 rs854560 TT genotype compared to those with the AA+AT genotypes. Plasma levels of HDL were significantly decreased in patients with the TRIB1 rs17321515 AA+AG genotypes compared to those with the GG genotype, as well as in patients with the TRIB1 rs2954029 AA+AT genotypes compared to those with the TT genotype. Our results suggest that the analysed polymorphisms are not risk factors for unstable angina in the Polish population. However, the results of this study indicate an association between the PON1 rs662, rs854560 and TRIB1 rs17321515, rs2954029 polymorphisms with lipid parameters in patients with coronary artery disease.
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Affiliation(s)
- Damian Malinowski
- Department of Pharmacokinetics and Therapeutic Drug Monitoring, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Krzysztof Safranow
- Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Andrzej Pawlik
- Department of Physiology, Pomeranian Medical University, 70-111 Szczecin, Poland
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Yao Y, Zhou M, Tan Q, Liang R, Guo Y, Wang D, Wang B, Xie Y, Yin H, Yang S, Shang B, You X, Cao X, Fan L, Ma J, Chen W. Associations of polychlorinated biphenyls exposure, lifestyle, and genetic susceptibility with dyslipidemias: Evidence from a general Chinese population. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134073. [PMID: 38552393 DOI: 10.1016/j.jhazmat.2024.134073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 02/27/2024] [Accepted: 03/17/2024] [Indexed: 04/25/2024]
Abstract
Polychlorinated biphenyls (PCBs) are endocrine-disrupting chemicals that have been associated with various adverse health conditions. Herein we explored the associations of PCBs with dyslipidemia and further assessed the modification effect of genetic susceptibility and lifestyle factors. Six serum PCBs (PCB-28, 101, 118, 138, 153, 180) were determined in 3845 participants from the Wuhan-Zhuhai cohort. Dyslipidemia, including hyper-total cholesterol (HyperTC), hyper-triglyceride (HyperTG), hyper-low density lipoprotein cholesterol (HyperLDL-C), and hypo-high density lipoprotein cholesterol (HypoHDL-C) were determined, and lipid-specific polygenic risk scores (PRS) and healthy lifestyle score were constructed. We found that all six PCB congeners were positively associated with the prevalence of dyslipidemias, and ΣPCB level was associated with HyperTC, HyperTG, and HyperLDL-C in dose-response manners. Compared with the lowest tertiles of ΣPCB, the odds ratios (95% confidence intervals) in the highest tertiles were 1.490 (1.258, 1.765) for HyperTC, 1.957 (1.623, 2.365) for HyperTG, and 1.569 (1.316, 1.873) for HyperLDL-C, respectively. Compared with those with low ΣPCB, healthy lifestyle, and low genetic risk, participants with high ΣPCB, unfavorable lifestyle, and high genetic risk had the highest odds of HyperTC, HyperTG, and HyperLDL-C. Our study provided evidence that high PCB exposure exacerbated the association of genetic risk and unhealthy lifestyle with dyslipidemia.
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Affiliation(s)
- Yuxin Yao
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Min Zhou
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Qiyou Tan
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Ruyi Liang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yanjun Guo
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Dongming Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Bin Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yujia Xie
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Haoyu Yin
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shiyu Yang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Bingxin Shang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xiaojie You
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xiuyu Cao
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lieyang Fan
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jixuan Ma
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
| | - Weihong Chen
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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Effect of TRIB1 Variant on Lipid Profile and Coronary Artery Disease: A Systematic Review and Meta-Analysis. Cardiovasc Ther 2023; 2023:4444708. [PMID: 36714195 PMCID: PMC9842430 DOI: 10.1155/2023/4444708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/20/2022] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
Background Emerging evidence indicates tribbles homolog 1 (Trib1) protein may be involved in lipid metabolism regulation and coronary artery disease (CAD) pathogenesis. However, whether TRIB1 gene variants affect lipid levels and CAD remains elusive, this study is aimed at clarifying the effect of TRIB1 variants on lipid profile and CAD. Methods By searching PubMed and Cochrane databases for studies published before December 18, 2022, a total of 108,831 individuals were included for the analysis. Results The outcomes of the analysis on all individuals showed that the A allele carriers of rs17321515 and rs2954029 variants had higher low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) levels than the noncarriers. Consistently, a higher CAD risk was observed in the A allele carriers. Subgroup analysis indicated that increased LDL-C, TC, and CAD risk were observed in Asian population. Conclusions Variants of TRIB1 (i.e., rs17321515 and rs2954029) may serve as causal genetic markers for dyslipidemia and CAD in Asian population.
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Luo JY, Liu F, Zhang T, Tian T, Luo F, Li XM, Yang YN. Association of NFKB1 gene rs28362491 mutation with the occurrence of major adverse cardiovascular events. BMC Cardiovasc Disord 2022; 22:313. [PMID: 35831800 PMCID: PMC9281072 DOI: 10.1186/s12872-022-02755-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Several studies have reported that NFKB1 gene rs28362491 polymorphism was associated with susceptibility to coronary heart disease in populations of different genetic backgrounds. To date, there have been no studies on the association between NFKB1 gene rs28362491 polymorphism and the occurrence of major adverse cardiac and cerebrovascular event (MACCE). The present study was to explore the relationship between NFKB1 gene rs28362491 polymorphism and MACCEs to investigate whether identifying NFKB1 gene polymorphism is beneficial to evaluating MACCE risks and patients' prognoses. METHODS We recruited 257 high-risk of cardiovascular disease patients with chest pain or precordial discomfort. The SNPscan™ were used to analyze the NFKB1 gene rs28362491 polymorphism. All patients were followed up in the clinic or by telephone interview for MACCEs. RESULTS During the followed-up time (mean: 30.1 months) 49 patients had MACCEs (19.1%). Patients with the different genotypes of NFKB1 rs28362491 had different incidence rate of MACCE. The incidence of MACCE in patients carried II, ID and DD genotype was 16.5%, 15.9%, 32.6%, respectively. Log-rank analysis showed that the survival rate in patients with NFKB1 rs28362491 DD genotype was much lower than that in II or ID genotype carriers (P = 0.034). After excluding the influence of traditional risk factors of MACCEs, Cox regression showed that the DD genotype carriers had 2.294-fold relative risk of MACCEs comparing with patients carried II or ID genotype. CONCLUSION The NFKB1 gene rs28362491 mutant was an independent predictor of worse long-term prognosis for MACCEs. Therefore, identifying NFKB1 gene rs28362491 mutant may be used as a good way for guiding the standardized management of patients with high-risk of cardiovascular diseases.
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Affiliation(s)
- Jun-Yi Luo
- Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan South Road, Urumqi, 830054, Xinjiang, China
| | - Fen Liu
- Xinjiang Key Laboratory of Cardiovascular Disease Research, Urumqi, Xinjiang, China
| | - Tong Zhang
- Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan South Road, Urumqi, 830054, Xinjiang, China
| | - Ting Tian
- Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan South Road, Urumqi, 830054, Xinjiang, China
| | - Fan Luo
- Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan South Road, Urumqi, 830054, Xinjiang, China
| | - Xiao-Mei Li
- Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, 137 Liyushan South Road, Urumqi, 830054, Xinjiang, China.
| | - Yi-Ning Yang
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Urumqi, 830054, Xinjiang, China.
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Association of protein function-altering variants with cardiometabolic traits: the strong heart study. Sci Rep 2022; 12:9317. [PMID: 35665752 PMCID: PMC9167281 DOI: 10.1038/s41598-022-12866-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022] Open
Abstract
Clinical and biomarker phenotypic associations for carriers of protein function-altering variants may help to elucidate gene function and health effects in populations. We genotyped 1127 Strong Heart Family Study participants for protein function-altering single nucleotide variants (SNV) and indels selected from a low coverage whole exome sequencing of American Indians. We tested the association of each SNV/indel with 35 cardiometabolic traits. Among 1206 variants (average minor allele count = 20, range of 1 to 1064), ~ 43% were not present in publicly available repositories. We identified seven SNV-trait significant associations including a missense SNV at ABCA10 (rs779392624, p = 8 × 10-9) associated with fasting triglycerides, which gene product is involved in macrophage lipid homeostasis. Among non-diabetic individuals, missense SNVs at four genes were associated with fasting insulin adjusted for BMI (PHIL, chr6:79,650,711, p = 2.1 × 10-6; TRPM3, rs760461668, p = 5 × 10-8; SPTY2D1, rs756851199, p = 1.6 × 10-8; and TSPO, rs566547284, p = 2.4 × 10-6). PHIL encoded protein is involved in pancreatic β-cell proliferation and survival, and TRPM3 protein mediates calcium signaling in pancreatic β-cells in response to glucose. A genetic risk score combining increasing insulin risk alleles of these four genes was associated with 53% (95% confidence interval 1.09, 2.15) increased odds of incident diabetes and 83% (95% confidence interval 1.35, 2.48) increased odds of impaired fasting glucose at follow-up. Our study uncovered novel gene-trait associations through the study of protein-coding variants and demonstrates the advantages of association screenings targeting diverse and high-risk populations to study variants absent in publicly available repositories.
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Ma J, Hao X, Nie X, Yang S, Zhou M, Wang D, Wang B, Cheng M, Ye Z, Xie Y, Wang C, Chen W. Longitudinal relationships of polycyclic aromatic hydrocarbons exposure and genetic susceptibility with blood lipid profiles. ENVIRONMENT INTERNATIONAL 2022; 164:107259. [PMID: 35500530 DOI: 10.1016/j.envint.2022.107259] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 03/22/2022] [Accepted: 04/20/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVE We aim to analyze the effects of polycyclic aromatic hydrocarbons (PAHs) exposure and genetic predisposition on blood lipid through a longitudinal epidemiological study. METHODS We enrolled 4,356 observations who participated at baseline (n = 2,435) and 6-year follow-up (n = 1,921) from Wuhan-Zhuhai cohort. Ten urinary PAHs metabolites and blood lipid (i.e., total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], and high-density lipoprotein cholesterol [HDL-C]) were measured at both baseline and follow-up. The polygenic risk scores (PRS) of blood lipid were constructed by the corresponding genome-wide association studies. Linear mixed models were fit to identify associations between urinary PAHs metabolites, blood lipid, and lipid-PRSs in the repeated-measure analysis. Besides, longitudinal relationships of blood lipid with urinary PAHs metabolites and respective lipid-PRSs were examined by using linear regression models. RESULTS Compared with subjects who had persistently low urinary total hydroxyphenanthrene (ΣOHPh), those with persistently high levels had an average increase of 0.137 mmol/l for TC and 0.129 mmol/l for LDL-C over 6 years. Each 1-unit increase of TC-, TG-, LDL-C-, and HDL-C-specific PRS were associated with an average increase of 0.438 mmol/l for TC, 0.264 mmol/l for TG, 0.198 mmol/l for LDL-C, and 0.043 mmol/l for HDL-C over 6 years, respectively. Compared with subjects who had low genetic risk and persistently low ΣOHPh, subjects with high LDL-specific PRS and persistently high ΣOHPh had an average increase of 0.652 mmol/l for LDL-C. CONCLUSIONS Our results suggest that high-level ΣOHPh exposure is associated with an average increase of LDL-C over 6 years, and those relationships can be aggravated by a higher LDL-C-genetic risk. No significant relationships were observed between other PAHs metabolites (including hydroxynaphthalene, hydroxyfluorene, and hydroxypyrene) and blood lipid changes over 6 years. Our findings emphasize the importance of preventing PAHs exposure, particularly among those with a higher genetic predisposition of hyperlipidemia.
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Affiliation(s)
- Jixuan Ma
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xingjie Hao
- Department of Epidemiology & Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xiuquan Nie
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shijie Yang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Min Zhou
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Dongming Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Bin Wang
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Man Cheng
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Zi Ye
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yujia Xie
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Chaolong Wang
- Department of Epidemiology & Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
| | - Weihong Chen
- Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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10
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Zhao JV, Liu F, Schooling CM, Li J, Gu D, Lu X. Using genetics to assess the association of commonly used antihypertensive drugs with diabetes, glycaemic traits and lipids: a trans-ancestry Mendelian randomisation study. Diabetologia 2022; 65:695-704. [PMID: 35080656 DOI: 10.1007/s00125-021-05645-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/10/2021] [Indexed: 12/17/2022]
Abstract
AIMS/HYPOTHESIS Diabetes and hyperlipidaemia are common comorbidities in people with hypertension. Despite similar protective effects on CVD, different classes of antihypertensive drugs have different effects on CVD risk factors, including diabetes, glucose metabolism and lipids. However, these pleiotropic effects have not been assessed in long-term, large randomised controlled trials, especially for East Asians. METHODS We used Mendelian randomisation to obtain unconfounded associations of ACE inhibitors, β-blockers (BBs) and calcium channel blockers (CCBs). Specifically, we used genetic variants in drug target genes and related to systolic BP in Europeans and East Asians, and applied them to the largest available genome-wide association studies of diabetes (74,124 cases and 824,006 controls in Europeans, 77,418 cases and 356,122 controls in East Asians), blood glucose levels, HbA1c, and lipids (LDL-cholesterol, HDL-cholesterol and triacylglycerols) (approximately 0.5 million Europeans and 0.1 million East Asians). We used coronary artery disease (CAD) as a control outcome and used different genetic instruments and analysis methods as sensitivity analyses. RESULTS As expected, genetically proxied ACE inhibition, BBs and CCBs were related to lower risk of CAD in both ancestries. Genetically proxied ACE inhibition was associated with a lower risk of diabetes (OR 0.85, 95% CI 0.78-0.93), and genetic proxies for BBs were associated with a higher risk of diabetes (OR 1.05, 95% CI 1.02-1.09). The estimates were similar in East Asians, and were corroborated by systematic review and meta-analyses of randomised controlled trials. In both ancestries, genetic proxies for BBs were associated with lower HDL-cholesterol and higher triacylglycerols, and genetic proxies for CCBs were associated with higher LDL-cholesterol. The estimates were robust to the use of different genetic instruments and analytical methods. CONCLUSIONS/INTERPRETATION Our findings suggest protective association of genetically proxied ACE inhibition with diabetes, while genetic proxies for BBs and CCBs possibly relate to an unfavourable metabolic profile. Developing a deeper understanding of the pathways underlying these diverse associations would be worthwhile, with implications for drug repositioning as well as optimal CVD prevention and treatment strategies in people with hypertension, diabetes and/or hyperlipidaemia.
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Affiliation(s)
- Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
| | - Fangchao Liu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Jianxin Li
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongfeng Gu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Xiangfeng Lu
- Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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11
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Wang T, Qiao J, Zhang S, Wei Y, Zeng P. Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models. Brief Bioinform 2022; 23:6535679. [PMID: 35212359 DOI: 10.1093/bib/bbac038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/22/2022] [Accepted: 02/07/2021] [Indexed: 11/14/2022] Open
Abstract
Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Jiahao Qiao
- Department of Biostatistics at Xuzhou Medical University, China
| | - Shuo Zhang
- Department of Biostatistics at Xuzhou Medical University, China
| | - Yongyue Wei
- Department of Biostatistics at Nanjing Medical University, China
| | - Ping Zeng
- Department of Biostatistics, Center for Medical Statistics and Data Analysis and Key Laboratory of Human Genetics and Environmental Medicine at Xuzhou Medical University, China
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12
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Wu Y, Xin J, Loehrer EA, Jiang X, Yuan Q, Christiani DC, Shi H, Liu L, Li S, Wang M, Chu H, Du M, Zhang Z. High-density lipoprotein, low-density lipoprotein and triglyceride levels and upper gastrointestinal cancers risk: a trans-ancestry Mendelian randomization study. Eur J Clin Nutr 2022; 76:995-1002. [DOI: 10.1038/s41430-022-01078-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 12/18/2021] [Accepted: 01/12/2022] [Indexed: 01/02/2023]
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13
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Fan HY, Huang YT, Chen YY, Hsu JB, Li HY, Su TC, Lin HJ, Chien KL, Chen YC. Systolic blood pressure as the mediator of the effect of early menarche on the risk of coronary artery disease: A Mendelian randomization study. Front Cardiovasc Med 2022; 9:1023355. [PMID: 36698922 PMCID: PMC9868731 DOI: 10.3389/fcvm.2022.1023355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Background Menarche timing may not be directly associated with the risk of coronary artery disease (CAD). Therefore, we investigated the roles of metabolic factors in explaining the effect of age at menarche on CAD risk. Methods We identified women with age at menarche and CAD by using three analytical methods: Mendelian randomization (MR), logistic regression analysis, and Cox proportional hazard regression. The first two analyses were performed in the Taiwan Biobank (N = 71,923) study, and the last analysis was performed in the Chin-Shan Community Cardiovascular Cohort study (N = 1,598). We further investigated the role of metabolic factors in mediating the effect of age at menarche on CAD risk by using three complementary methods with mediation analyses. Results One standard deviation of earlier age at menarche was associated with a 2% higher CAD risk [odds ratio = 1.02, 95% confidence interval (CI) = 1.001-1.03] in the MR analysis, an 11% higher risk (odds ratio = 1.11, 95% CI = 1.02-1.21) in the logistic regression analysis, and a 57% higher risk (hazard ratio = 1.57, 95% CI = 1.12-2.19) in the Cox proportional hazard regression. All the analyses consistently supported the role of systolic blood pressure in mediating this effect. The MR results indicated that 29% (95% CI = 26%-32%) of the effect of genetically predicted earlier age at menarche on CAD risk was mediated by genetically predicted systolic blood pressure. Conclusion The results obtained using different analytical methods suggest that interventions aimed at lowering systolic blood pressure can reduce the cases of CAD attributable to earlier age at menarche.
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Affiliation(s)
- Hsien-Yu Fan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.,Department of Family Medicine, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yen-Tsung Huang
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.,Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.,Department of Mathematics, National Taiwan University, Taipei, Taiwan
| | - Yun-Yu Chen
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.,Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan.,Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,Cardiovascular Research Center, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Justin BoKai Hsu
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Hung-Yuan Li
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ta-Chen Su
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Hung-Ju Lin
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Kuo-Liong Chien
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.,Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yang-Ching Chen
- Department of Family Medicine, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Metabolism and Obesity Sciences, Taipei Medical University, Taipei, Taiwan
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14
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Zhou YG, Yin RX, Huang F, Wu JZ, Chen WX, Cao XL. DGAT2-MOGAT2 SNPs and Gene-Environment Interactions on Serum Lipid Profiles and the Risk of Ischemic Stroke. Front Cardiovasc Med 2021; 8:685970. [PMID: 34901200 PMCID: PMC8654148 DOI: 10.3389/fcvm.2021.685970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/31/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The genetic susceptibility to ischemic stroke (IS) is still not well-understood. Recent genome-wide association studies (GWASes) found that several single nucleotide polymorphisms (SNPs) in the Diacylglycerol acyltransferase 2 gene (DGAT2) and monoacylglycerol O-acyltransferase 2 (MOGAT2) cluster were associated with serum lipid levels. However, the association between the DGAT2-MOGAT2 SNPs and serum lipid phenotypes has not yet been verified in the Chinese people. Therefore, the present study was to determine the DGAT2-MOGAT2 SNPs and gene-environment interactions on serum lipid profiles and the risk of IS. Methods: Genotyping of 5 SNPs (DGAT2 rs11236530, DGAT2 rs3060, MOGAT2 rs600626, MOGAT2 rs609379, and MOGAT2 rs10899104) in 544 IS patients and 561 healthy controls was performed by the next-generation sequencing technologies. The association between genotypes and serum lipid data was determined by analysis of covariance, and a corrected P-value was adopted after Bonferroni correction. Unconditional logistic regression analysis was performed to assess the association between genotypes and the risk of IS after adjustment of potential confounders. Results: The rs11236530A allele was associated with increased risk of IS (CA/AA vs. CC, OR = 1.45, 95%CI = 1.12-1.88, P = 0.0044), whereas the rs600626G-rs609379A-rs10899104G haplotype was associated with decreased risk of IS (adjusted OR = 0.67, 95% CI = 0.48-0.93, P = 0.018). The rs11236530A allele carriers had lower high-density lipoprotein cholesterol (HDL-C) concentrations than the rs11236530A allele non-carriers (P < 0.001). The interactions of rs11236530-smoking, rs3060-smoking and rs10899104-smoking influenced serum apolipoprotein B levels, whereas the interactions of rs11236530- and rs3060-alcohol affected serum HDL-C levels (P I < 0.004-0.001). The interaction of rs600626G-rs609379A-rs10899104G-alcohol (OR = 0.41, 95% CI = 0.22-0.76) and rs600626G-rs609379C-rs10899104T-alcohol (OR = 0.12, 95% CI = 0.04-0.36) decreased the risk of IS (P I < 0.0001). Conclusions: The rs11236530A allele was associated with decreased serum HDL-C levels in controls and increased risk of IS in patient group. The rs600626G-rs609379A-rs10899104G haplotype, the rs600626G-rs 609379A-rs10899104G-alcohol and rs600626G-rs609379C-rs10899104T-alcohol interactions were associated with decreased risk of IS. The rs11236530 SNP may be a genetic marker for IS in our study populations.
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Affiliation(s)
- Yong-Gang Zhou
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Rui-Xing Yin
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Feng Huang
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Jin-Zhen Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Wu-Xian Chen
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
| | - Xiao-Li Cao
- Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning, China
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Abstract
PURPOSE OF REVIEW Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. RECENT FINDINGS More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.
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Affiliation(s)
- Germán D. Carrasquilla
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Mærsk Building, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Malene Revsbech Christiansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Mærsk Building, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Mærsk Building, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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16
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Hormozdiari F, Jung J, Eskin E, J. Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing. Genome Biol 2021; 22:128. [PMID: 33931127 PMCID: PMC8086090 DOI: 10.1186/s13059-021-02353-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/15/2021] [Indexed: 11/10/2022] Open
Abstract
In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.
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Affiliation(s)
- Farhad Hormozdiari
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115 MA USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Junghyun Jung
- Department of Life Science, Dongguk University-Seoul, Seoul, 04620 South Korea
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, 90095 CA USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, 90095 CA USA
| | - Jong Wha J. Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620 South Korea
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17
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Guerra-García MT, Moreno-Macías H, Ochoa-Guzmán A, Ordoñez-Sánchez ML, Rodríguez-Guillen R, Vázquez-Cárdenas P, Ortíz-Ortega VM, Peimbert-Torres M, Aguilar-Salinas CA, Tusié-Luna MT. The -514C>T polymorphism in the LIPC gene modifies type 2 diabetes risk through modulation of HDL-cholesterol levels in Mexicans. J Endocrinol Invest 2021; 44:557-565. [PMID: 32617858 DOI: 10.1007/s40618-020-01346-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/25/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE Both type 2 diabetes (T2D) and low levels of high-density lipoprotein cholesterol (HDL-C) are very prevalent conditions among Mexicans. Genetic variants in the LIPC gene have been associated with both conditions. This study aimed to evaluate the association of the -514C < T (rs1800588) LIPC gene polymorphism with different metabolic traits, particularly the effects of this polymorphism on HDL-C plasma levels and T2D risk. METHODS Mediation analysis was used to assess the direct and indirect effects of the -514C>T LIPC gene variant on HDL-C levels, T2D risk, and body mass index (BMI), in 2105 Mexican mestizo participants. We also assessed the functional effect of the -514C>T LIPC variant on the promoter activity of a reporter gene in the HepG2 cell line. RESULTS Direct effects show that the -514C>T LIPC polymorphism is significantly associated with increased HDL-C plasma levels (β = 0.03; p < 0.001). The -514C>T variant resulted in an indirect protective effect on T2D risk through increasing HDL-C levels (β = - 0.03; p < 0.001). Marginal direct association between -514C>T and T2D was found (β = 0.08; p = 0.06). Variables directly influencing T2D status were European ethnicity (β = - 7.20; p < 0.001), age (β = 0.04; p < 0.001), gender (β = - 0.15; p = 0.017) and HDL-C (β = - 1.07; p < 0.001). In addition, we found that the -514C>T variant decreases the activity of LIPC promoter by 90% (p < 0.001). CONCLUSIONS The -514C>T polymorphism was not directly associated with T2D risk. HDL-C acts as a mediator between -514C>T LIPC gene variant and T2D risk in the Mexican population.
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Affiliation(s)
- M T Guerra-García
- Unit of Molecular Biology and Genomic Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez, Sección XVI, Tlalpan, 14080, Mexico City, Mexico
| | - H Moreno-Macías
- Unit of Molecular Biology and Genomic Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez, Sección XVI, Tlalpan, 14080, Mexico City, Mexico
- Economy Department, Universidad Autónoma Metropolitana, Mexico City, Mexico
| | - A Ochoa-Guzmán
- Unit of Molecular Biology and Genomic Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez, Sección XVI, Tlalpan, 14080, Mexico City, Mexico
| | - M L Ordoñez-Sánchez
- Unit of Molecular Biology and Genomic Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez, Sección XVI, Tlalpan, 14080, Mexico City, Mexico
| | - R Rodríguez-Guillen
- Unit of Molecular Biology and Genomic Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez, Sección XVI, Tlalpan, 14080, Mexico City, Mexico
| | - P Vázquez-Cárdenas
- Obesity Clinic, Hospital General Dr. Manuel Gea González, Mexico City, Mexico
| | - V M Ortíz-Ortega
- Department of Physiology of Nutrition, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - M Peimbert-Torres
- Nature Sciences Department, Universidad Autónoma Metropolitana, Mexico City, Mexico
| | - C A Aguilar-Salinas
- Division of Nutrition, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - M T Tusié-Luna
- Unit of Molecular Biology and Genomic Medicine, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga #15, Belisario Domínguez, Sección XVI, Tlalpan, 14080, Mexico City, Mexico.
- Instituto de Investigaciones Biomédicas, Univesidad Nacional Autónoma de México, Mexico City, Mexico.
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18
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Tam CHT, Lim CKP, Luk AOY, Ng ACW, Lee HM, Jiang G, Lau ESH, Fan B, Wan R, Kong APS, Tam WH, Ozaki R, Chow EYK, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JYY, Tsang MW, Kam G, Lau IT, Li JKY, Yeung VTF, Lau E, Lo S, Fung S, Cheng YL, Chow CC, Hu M, Yu W, Tsui SKW, Huang Y, Lan H, Szeto CC, Tang NLS, Ng MCY, So WY, Tomlinson B, Chan JCN, Ma RCW. Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians. Genome Med 2021; 13:29. [PMID: 33608049 PMCID: PMC7893928 DOI: 10.1186/s13073-021-00831-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 01/12/2021] [Indexed: 11/25/2022] Open
Abstract
Background The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear. Methods We used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients. Results Our PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10− 103 < P < 1.3 × 10− 75) and 3-year lipid changes (1.4 × 10− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, Ptrend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association. Conclusions The PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00831-z.
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Affiliation(s)
- Claudia H T Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.,CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.,CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong, China
| | - Andrea O Y Luk
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.,CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Alex C W Ng
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Heung-Man Lee
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Eric S H Lau
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Baoqi Fan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Raymond Wan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Alice P S Kong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Wing-Hung Tam
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - Risa Ozaki
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Elaine Y K Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Ka-Fai Lee
- Department of Medicine and Geriatrics, Kwong Wah Hospital, Yau Ma Tei, Hong Kong, China
| | - Shing-Chung Siu
- Diabetes Centre, Tung Wah Eastern Hospital, Causeway Bay, Hong Kong, China
| | - Grace Hui
- Diabetes Centre, Tung Wah Eastern Hospital, Causeway Bay, Hong Kong, China
| | - Chiu-Chi Tsang
- Diabetes and Education Centre, Alice Ho Miu Ling Nethersole Hospital, Tai Po, Hong Kong, China
| | - Kam-Piu Lau
- North District Hospital, Sheung Shui, Hong Kong, China
| | - Jenny Y Y Leung
- Department of Medicine and Geriatrics, Ruttonjee Hospital, Wan Chai, Hong Kong, China
| | - Man-Wo Tsang
- Department of Medicine and Geriatrics, United Christian Hospital, Kwun Tong, Hong Kong, China
| | - Grace Kam
- Department of Medicine and Geriatrics, United Christian Hospital, Kwun Tong, Hong Kong, China
| | - Ip-Tim Lau
- Tseung Kwan O Hospital, Tseung Kwan O, Hong Kong, China
| | - June K Y Li
- Department of Medicine, Yan Chai Hospital, Tsuen Wan, Hong Kong, China
| | - Vincent T F Yeung
- Centre for Diabetes Education and Management, Our Lady of Maryknoll Hospital, Wong Tai Sin, Hong Kong, China
| | - Emmy Lau
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong, China
| | - Stanley Lo
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong, China
| | - Samuel Fung
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Lai Chi Kok, Hong Kong, China
| | - Yuk-Lun Cheng
- Department of Medicine, Alice Ho Miu Ling Nethersole Hospital, Tai Po, Hong Kong, China
| | - Chun-Chung Chow
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Miao Hu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Weichuan Yu
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Stephen K W Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong, China
| | - Yu Huang
- School of Biomedical Sciences, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong, China
| | - Huiyao Lan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Cheuk-Chun Szeto
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Nelson L S Tang
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Maggie C Y Ng
- Department of Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Wing-Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.,CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China. .,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China. .,CUHK-SJTU Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong, China. .,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
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19
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Functional Haplotype of LIPC Induces Triglyceride-Mediated Suppression of HDL-C Levels According to Genome-Wide Association Studies. Genes (Basel) 2021; 12:genes12020148. [PMID: 33499410 PMCID: PMC7910859 DOI: 10.3390/genes12020148] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/11/2021] [Accepted: 01/19/2021] [Indexed: 01/08/2023] Open
Abstract
Hepatic lipase (encoded by LIPC) is a glycoprotein in the triacylglycerol lipase family and mainly synthesized in and secreted from the liver. Previous studies demonstrated that hepatic lipase is crucial for reverse cholesterol transport and modulating metabolism and the plasma levels of several lipoproteins. This study was conducted to investigate the suppression effect of high-density lipoprotein cholesterol (HDL-C) levels in a genome-wide association study and explore the possible mechanisms linking triglyceride (TG) to LIPC variants and HDL-C. Genome-wide association data for TG and HDL-C were available for 4657 Taiwan-biobank participants. The prevalence of haplotypes in the LIPC promoter region and their effects were calculated. The cloned constructs of the haplotypes were expressed transiently in HepG2 cells and evaluated in a luciferase reporter assay. Genome-wide association analysis revealed that HDL-C was significantly associated with variations in LIPC after adjusting for TG. Three haplotypes (H1: TCG, H2: CTA and H3: CCA) in LIPC were identified. H2: CTA was significantly associated with HDL-C levels and H1: TCG suppressed HDL-C levels when a third factor, TG, was included in mediation analysis. The luciferase reporter assay further showed that the H2: CTA haplotype significantly inhibited luciferase activity compared with the H1: TCG haplotype. In conclusion, we identified a suppressive role for TG in the genome-wide association between LIPC and HDL-C. A functional haplotype of hepatic lipase may reduce HDL-C levels and is suppressed by TG.
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20
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Pirim D, Bunker CH, Hokanson JE, Hamman RF, Demirci FY, Kamboh MI. Hepatic lipase (LIPC) sequencing in individuals with extremely high and low high-density lipoprotein cholesterol levels. PLoS One 2020; 15:e0243919. [PMID: 33326441 PMCID: PMC7743991 DOI: 10.1371/journal.pone.0243919] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/01/2020] [Indexed: 02/06/2023] Open
Abstract
Common variants in the hepatic lipase (LIPC) gene have been shown to be associated with plasma lipid levels; however, the distribution and functional features of rare and regulatory LIPC variants contributing to the extreme lipid phenotypes are not well known. This study was aimed to catalogue LIPC variants by resequencing the entire LIPC gene in 95 non-Hispanic Whites (NHWs) and 95 African blacks (ABs) with extreme HDL-C levels followed by in silico functional analyses. A total of 412 variants, including 43 novel variants were identified; 56 were unique to NHWs and 234 were unique to ABs. Seventy-eight variants in NHWs and 89 variants in ABs were present either in high HDL-C group or low HDL-C group. Two non-synonymous variants (p.S289F, p.T405M), found in NHWs with high HDL-C group were predicted to have damaging effect on LIPC protein by SIFT, MT2 and PP2. We also found several non-coding variants that possibly reside in the circRNA and lncRNA binding sites and may have regulatory potential, as identified in rSNPbase and RegulomeDB databases. Our results shed light on the regulatory nature of rare and non-coding LIPC variants as well as suggest their important contributions in affecting the extreme HDL-C phenotypes.
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Affiliation(s)
- Dilek Pirim
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Molecular Biology and Genetics, Faculty of Arts & Science, Bursa Uludag University, Gorukle, Bursa, Turkey
| | - Clareann H. Bunker
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - John E. Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, United States of America
| | - Richard F. Hamman
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, United States of America
| | - F. Yesim Demirci
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - M. Ilyas Kamboh
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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21
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Xu Q, Qi W, Zhang Y, Wang Q, Ding S, Han X, Zhao Y, Song X, Zhao T, Zhou L, Ye L. DNA methylation of JAK3/STAT5/PPARγ regulated the changes of lipid levels induced by di (2-ethylhexyl) phthalate and high-fat diet in adolescent rats. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:30232-30242. [PMID: 32451896 DOI: 10.1007/s11356-020-08976-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 04/21/2020] [Indexed: 06/11/2023]
Abstract
Di (2-ethylhexyl) phthalate (DEHP) and high-fat diet (HFD) could induce lipid metabolic disorder. This study was undertaken to identify the effect of DNA methylation of JAK3/STAT5/PPARγ on lipid metabolic disorder induced by DEHP and HFD. Wistar rats were divided into a normal diet (ND) group and HFD group. Each diet group treated with DEHP (0, 5, 50, 500 mg/kg/d) for 8 weeks' gavage. The DNA-methylated levels of PPARγ, JAK3, STAT5a, and STAT5b in rats' livers and adipose were analyzed with MethylTarget. The lipid levels of rats' livers and adipose were detected with ELISA. Results showed in ND group that the DNA methylation levels of PPARγ, JAK3 in livers, and STAT5b in adipose were lower in 500 mg/kg/d group than the control. And the level of total cholesterol (TC) in adipose was higher in 500 mg/kg/d group than the control. In HFD group, the DNA methylation level of JAK3 was the lowest in livers and the highest in adipose in 50 mg/kg/d group. And the level of TC in livers was the lowest in 50 mg/kg/d group. In the 500 mg/kg/d group, the DNA methylation level of STAT5b was lower in livers and higher in adipose in HFD group than that in ND group. And the levels of TC in livers were lower in HFD group than those in ND group. Therefore, DNA methylation of JAK3/STAT5/PPARγ regulated the changes in lipid levels induced by DEHP and HFD in adolescent rats.
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Affiliation(s)
- Qi Xu
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Wen Qi
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Yuezhu Zhang
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Qi Wang
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Shuang Ding
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Xu Han
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Yaming Zhao
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Xinyue Song
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Tianyang Zhao
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China
| | - Liting Zhou
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China.
| | - Lin Ye
- Department of Occupational and Environmental Health, School of Public Health, Jilin University, 1163 Xin Min Street, Changchun, 130021, China.
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22
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Yang C, Wan X, Lin X, Chen M, Zhou X, Liu J. CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information. Bioinformatics 2020; 35:1644-1652. [PMID: 30295737 DOI: 10.1093/bioinformatics/bty865] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Revised: 09/15/2018] [Accepted: 10/05/2018] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWASs) have been successful in identifying many genetic variants associated with complex traits. However, the mechanistic links between these variants and complex traits remain elusive. A scientific hypothesis is that genetic variants influence complex traits at the organismal level via affecting cellular traits, such as regulating gene expression and altering protein abundance. Although earlier works have already presented some scientific insights about this hypothesis and their findings are very promising, statistical methods that effectively harness multilayered data (e.g. genetic variants, cellular traits and organismal traits) on a large scale for functional and mechanistic exploration are highly demanding. RESULTS In this study, we propose a collaborative mixed model (CoMM) to investigate the mechanistic role of associated variants in complex traits. The key idea is built upon the emerging scientific evidence that genetic effects at the cellular level are much stronger than those at the organismal level. Briefly, CoMM combines two models: the first model relating gene expression with genotype and the second model relating phenotype with predicted gene expression using the first model. The two models are fitted jointly in CoMM, such that the uncertainty in predicting gene expression has been fully accounted. To demonstrate the advantages of CoMM over existing methods, we conducted extensive simulation studies, and also applied CoMM to analyze 25 traits in NFBC1966 and Genetic Epidemiology Research on Aging (GERA) studies by integrating transcriptome information from the Genetic European in Health and Disease (GEUVADIS) Project. The results indicate that by leveraging regulatory information, CoMM can effectively improve the power of prioritizing risk variants. Regarding the computational efficiency, CoMM can complete the analysis of NFBC1966 dataset and GERA datasets in 2 and 18 min, respectively. AVAILABILITY AND IMPLEMENTATION The developed R package is available at https://github.com/gordonliu810822/CoMM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China
| | - Xiang Wan
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Xinyi Lin
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Mengjie Chen
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jin Liu
- Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
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23
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Zhang YB, Sheng LT, Wei W, Guo H, Yang H, Min X, Guo K, Yang K, Zhang X, He M, Wu T, Pan A. Association of blood lipid profile with incident chronic kidney disease: A Mendelian randomization study. Atherosclerosis 2020; 300:19-25. [DOI: 10.1016/j.atherosclerosis.2020.03.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 02/14/2020] [Accepted: 03/25/2020] [Indexed: 01/06/2023]
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24
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Hsiung CN, Chang YC, Lin CW, Chang CW, Chou WC, Chu HW, Su MW, Wu PE, Shen CY. The Causal Relationship of Circulating Triglyceride and Glycated Hemoglobin: A Mendelian Randomization Study. J Clin Endocrinol Metab 2020; 105:5648095. [PMID: 31784746 DOI: 10.1210/clinem/dgz243] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 11/29/2019] [Indexed: 12/20/2022]
Abstract
CONTEXT The association between circulating triglyceride (TG) and glycated hemoglobin A1c (HbA1c), a biomarker for type 2 diabetes, has been widely addressed, but the causal direction of the relationship is still ambiguous. OBJECTIVE To confirm the causal relationship between TG and HbA1c by using bidirectional and 2-step Mendelian randomization (MR) approaches. METHODS We carried out a bidirectional MR approach using the summarized results from the public database to examine any potential causal effects between serum TG and HbA1c in 16 000 individuals of the Taiwan Biobank cohort. We used the MR estimate and the MR inverse variance-weighted method to reveal that relationship between TG and HbA1c. To further determine whether the DNA methylation at specific sequences mediate the causal pathway between TG and HbA1c, using the 2-step MR approach. RESULTS We identified that a single-unit increase in TG measured via log transformation of mg/dL data was associated with a significant increase of 10 units of HbA1c (95% CI = 1.05-18.95, P = 0.029). In contrast, the genetic determinants of HbA1c do not contribute to the amount of circulating TG (beta = 1.75, 95% CI = -11.50 to 14.90). Sensitivity analyses, included the weighted-median approach and MR-Egger regression, were performed to confirm no pleiotropic effect among these instrumental variables. Furthermore, we identified the genetic variant, rs1823200, is associated with both methylation of the CpG site adjacent to CADPS gene and HbA1c level. CONCLUSION Our study suggests that higher circulating TG can have an affect on genomic methylation status, ultimately causing elevated level of circulating HbA1c.
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Affiliation(s)
- Chia-Ni Hsiung
- Institute of Bioinformatics and Structure Biology, National Tsing Hua University, Hsinchu, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Yi-Cheng Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | | | | | - Wen-Cheng Chou
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Hou-Wei Chu
- Taiwan Biobank, Academia Sinica, Taipei, Taiwan
| | - Ming-Wei Su
- Taiwan Biobank, Academia Sinica, Taipei, Taiwan
| | - Pei-Ei Wu
- Taiwan Biobank, Academia Sinica, Taipei, Taiwan
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- College of Public Health, China Medical University, Taichung, Taiwan
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25
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Magosi LE, Goel A, Hopewell JC, Farrall M. Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease. Bioinformatics 2020; 36:552-557. [PMID: 31350884 PMCID: PMC7223261 DOI: 10.1093/bioinformatics/btz590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 06/19/2019] [Accepted: 07/24/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis. RESULTS Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals. AVAILABILITY AND IMPLEMENTATION Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lerato E Magosi
- Wellcome Centre for Human Genetics.,Division of Cardiovascular Medicine, Radcliffe Department of Medicine
| | - Anuj Goel
- Wellcome Centre for Human Genetics.,Division of Cardiovascular Medicine, Radcliffe Department of Medicine
| | - Jemma C Hopewell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Martin Farrall
- Wellcome Centre for Human Genetics.,Division of Cardiovascular Medicine, Radcliffe Department of Medicine
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26
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Genome-wide association study of metabolic syndrome in Korean populations. PLoS One 2020; 15:e0227357. [PMID: 31910446 PMCID: PMC6946588 DOI: 10.1371/journal.pone.0227357] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 12/17/2019] [Indexed: 12/24/2022] Open
Abstract
Metabolic syndrome (MetS) which is caused by obesity and insulin resistance, is well known for its predictive capability for the risk of type 2 diabetes mellitus and cardiovascular disease. The development of MetS is associated with multiple genetic factors, environmental factors and lifestyle. We performed a genome-wide association study to identify single-nucleotide polymorphism (SNP) related to MetS in large Korean population based samples of 1,362 subjects with MetS and 6,061 controls using the Axiom® Korean Biobank Array 1.0. We replicated the data in another sample including 502 subjects with MetS and 1,751 controls. After adjusting for age and sex, rs662799 located in the APOA5 gene were significantly associated with MetS. 15 SNPs in GCKR, C2orf16, APOA5, ZPR1, and BUD13 were associated with high triglyceride (TG). 14 SNPs in APOA5, ALDH1A2, LIPC, HERPUD1, and CETP, and 2 SNPs in MTNR1B were associated with low high density lipoprotein cholesterol (HDL-C) and high fasting blood glucose respectively. Among these SNPs, 6 TG SNPs: rs1260326, rs1260333, rs1919127, rs964184, rs2075295 and rs1558861 and 11 HDL-C SNPs: rs4775041, rs10468017, rs1800588, rs72786786, rs173539, rs247616, rs247617, rs3764261, rs4783961, rs708272, and rs7499892 were first discovered in Koreans. Additional research is needed to confirm these 17 novel SNPs in Korean population.
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27
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Akimoto S, Goto C, Kuriki K. Relationship between ethanol consumption and TBL2 rs17145738 on LDL-C concentration in Japanese adults: a four season 3-day weighed diet record study. BMC Nutr 2019; 5:61. [PMID: 32153974 PMCID: PMC7050859 DOI: 10.1186/s40795-019-0315-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 10/14/2019] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
LDL cholesterol (LDL-C) concentration is modified by dietary and genetic factors; however, little is known about the details of this relationship. Our aim was to investigate the associations taking into account dietary assessment methods, seasonal effects and missing values.
Methods
Study subjects completed food frequency questionnaires (FFQ) and supplied 3-day weighed dietary records (WDRs) and blood samples in four seasons. Approximately 660,000 single nucleotide polymorphisms (SNPs) were measured. Candidate SNPs related to LDL-C concentration were systematically selected. Multiple imputation was applied for missing values. A total of 312 repeated measures data were used for analyses. After adjusting for season and subjects as fixed and random effects, effects of nutrient intake and SNPs on LDL-C concentration were assessed according to three dietary assessment methods: the FFQ and first and four season 3-day WDRs (4 s-3d WDRs).
Results
For LDL-C concentration, ethanol consumption derived from all three dietary assessment methods was consistently associated (P < 0.09 for all). Positive and negative relationships were consistently shown with rs651007 and rs1160985 in the first and four seasons; but the latter remained after adjusting for total dietary fiber intake derived from the FFQ and 4 s-3d WDRs (P < 0.05, excepting the first 3-day WDRs). rs599839 was negatively associated after cholesterol intakes derived from the first and 4 s-3d WDRs were considered (P < 0.05 and 0.07, respectively). Each rs17145738 and ethanol consumption based on the 4 s-3d WDRs was related to LDL-C concentration (P < 0.05). Seasonal variations of LDL-C concentration were observed only in summer.
Conclusions
In contrast to nutrient intake, ethanol consumption was shown to be comprehensively related to LDL-C concentration, regardless of dietary assessment methods. Taking into account seasonal effects, critical relationships with LDL-C concentration for some SNPs, after adjustment for specific nutrients, were revealed. Our findings can be used to help to interpret the relationships between dietary and genetic factors on LDL-C concentration in large-scale epidemiological studies.
(10/10 keywords)
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Chen L, Chen XW, Huang X, Song BL, Wang Y, Wang Y. Regulation of glucose and lipid metabolism in health and disease. SCIENCE CHINA-LIFE SCIENCES 2019; 62:1420-1458. [PMID: 31686320 DOI: 10.1007/s11427-019-1563-3] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 10/15/2019] [Indexed: 02/08/2023]
Abstract
Glucose and fatty acids are the major sources of energy for human body. Cholesterol, the most abundant sterol in mammals, is a key component of cell membranes although it does not generate ATP. The metabolisms of glucose, fatty acids and cholesterol are often intertwined and regulated. For example, glucose can be converted to fatty acids and cholesterol through de novo lipid biosynthesis pathways. Excessive lipids are secreted in lipoproteins or stored in lipid droplets. The metabolites of glucose and lipids are dynamically transported intercellularly and intracellularly, and then converted to other molecules in specific compartments. The disorders of glucose and lipid metabolism result in severe diseases including cardiovascular disease, diabetes and fatty liver. This review summarizes the major metabolic aspects of glucose and lipid, and their regulations in the context of physiology and diseases.
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Affiliation(s)
- Ligong Chen
- School of Pharmaceutical Sciences, Beijing Advanced Innovation Center for Structural Biology, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Tsinghua University, Beijing, 100084, China.
| | - Xiao-Wei Chen
- State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Xun Huang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Bao-Liang Song
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
| | - Yan Wang
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
| | - Yiguo Wang
- MOE Key Laboratory of Bioinformatics, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
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29
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Giuliani C, Sazzini M, Pirazzini C, Bacalini MG, Marasco E, Ruscone GAG, Fang F, Sarno S, Gentilini D, Di Blasio AM, Crocco P, Passarino G, Mari D, Monti D, Nacmias B, Sorbi S, Salvarani C, Catanoso M, Pettener D, Luiselli D, Ukraintseva S, Yashin A, Franceschi C, Garagnani P. Impact of demography and population dynamics on the genetic architecture of human longevity. Aging (Albany NY) 2019; 10:1947-1963. [PMID: 30089705 PMCID: PMC6128422 DOI: 10.18632/aging.101515] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 07/26/2018] [Indexed: 02/07/2023]
Abstract
The study of the genetics of longevity has been mainly addressed by GWASs that considered subjects from different populations to reach higher statistical power. The "price to pay" is that population-specific evolutionary histories and trade-offs were neglected in the investigation of gene-environment interactions. We propose a new “diachronic” approach that considers processes occurred at both evolutionary and lifespan timescales. We focused on a well-characterized population in terms of evolutionary history (i.e. Italians) and we generated genome-wide data for 333 centenarians from the peninsula and 773 geographically-matched healthy individuals. Obtained results showed that: (i) centenarian genomes are enriched for an ancestral component likely shaped by pre-Neolithic migrations; (ii) centenarians born in Northern Italy unexpectedly clustered with controls from Central/Southern Italy suggesting that Neolithic and Bronze Age gene flow did not favor longevity in this population; (iii) local past adaptive events in response to pathogens and targeting arachidonic acid metabolism became favorable for longevity; (iv) lifelong changes in the frequency of several alleles revealed pleiotropy and trade-off mechanisms crucial for longevity. Therefore, we propose that demographic history and ancient/recent population dynamics need to be properly considered to identify genes involved in longevity, which can differ in different temporal/spatial settings.
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Affiliation(s)
- Cristina Giuliani
- Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology, University of Bologna, Bologna, Italy.,School of Anthropology and Museum Ethnography, University of Oxford, Oxford, UK.,Interdepartmental Center "L. Galvani," (CIG), University of Bologna, Bologna, Italy
| | - Marco Sazzini
- Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology, University of Bologna, Bologna, Italy
| | - Chiara Pirazzini
- IRCCS, Institute of Neurological Sciences of Bologna, Bologna, Italy
| | | | - Elena Marasco
- Interdepartmental Center "L. Galvani," (CIG), University of Bologna, Bologna, Italy.,Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy.,Applied Biomedical Research Center (CRBA), S. Orsola-Malpighi Polyclinic, Bologna, Italy
| | - Guido Alberto Gnecchi Ruscone
- Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology, University of Bologna, Bologna, Italy
| | - Fang Fang
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Stefania Sarno
- Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology, University of Bologna, Bologna, Italy
| | - Davide Gentilini
- Istituto Auxologico Italiano IRCCS, Cusano Milanino, Milan, Italy
| | | | - Paolina Crocco
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Daniela Mari
- Geriatric Unit, Department of Medical Sciences and Community Health, Milan, Italy.,Fondazione Ca' Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy
| | - Daniela Monti
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Florence, Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy.,IRCCS Don Gnocchi, Florence, Italy
| | - Carlo Salvarani
- Azienda Ospedaliera-IRCCS, Reggio Emilia, Italy.,Department of Surgical, Medical, Dental and Morphological Sciences with Interest Transplant, Oncological and Regenerative Medicine, , Italy
| | | | - Davide Pettener
- Department of Biological, Geological, and Environmental Sciences (BiGeA), Laboratory of Molecular Anthropology and Centre for Genome Biology, University of Bologna, Bologna, Italy
| | - Donata Luiselli
- Department for the Cultural Heritage (DBC), University of Bologna, Ravenna, Italy
| | - Svetlana Ukraintseva
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Anatoliy Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC 27708, USA
| | - Claudio Franceschi
- IRCCS, Institute of Neurological Sciences of Bologna, Bologna, Italy.,Co-senior authors
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy.,Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet at Huddinge University Hospital, S-141 86 Stockholm, Sweden.,CNR Institute of Molecular Genetics, Unit of Bologna, Bologna, Italy.,Rizzoli Orthopaedic Institute, Laboratory of Cell Biology, Bologna, Italy.,Co-senior authors
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30
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Zeng P, Zhou X. Causal effects of blood lipids on amyotrophic lateral sclerosis: a Mendelian randomization study. Hum Mol Genet 2019; 28:688-697. [PMID: 30445611 PMCID: PMC6360326 DOI: 10.1093/hmg/ddy384] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 11/03/2018] [Indexed: 12/11/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disorder that is predicted to increase across the globe by ~70% in the following decades. Understanding the disease causal mechanism underlying ALS and identifying modifiable risks factors for ALS hold the key for the development of effective preventative and treatment strategies. Here, we investigate the causal effects of four blood lipid traits that include high-density lipoprotein, low-density lipoprotein (LDL), total cholesterol and triglycerides on the risk of ALS. By leveraging instrument variables from multiple large-scale genome-wide association studies in both European and East Asian populations, we carry out one of the largest and most comprehensive Mendelian randomization analyses performed to date on the causal relationship between lipids and ALS. Among the four lipids, we found that only LDL is causally associated with ALS and that higher LDL level increases the risk of ALS in both the European and East Asian populations. Specifically, the odds ratio of ALS per 1 standard deviation (i.e. 39.0 mg/dL) increase of LDL is estimated to be 1.14 [95% confidence interval (CI), 1.05–1.24; P = 1.38E-3] in the European population and 1.06 (95% CI, 1.00–1.12; P = 0.044) in the East Asian population. The identified causal relationship between LDL and ALS is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our study provides important evidence supporting the causal role of higher LDL on increasing the risk of ALS, paving ways for the development of preventative strategies for reducing the disease burden of ALS across multiple nations.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiang Zhou
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
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31
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Teng MS, Wu S, Hsu LA, Tzeng IS, Chou HH, Su CW, Ko YL. Pleiotropic association of LIPC variants with lipid and urinary 8-hydroxy deoxyguanosine levels in a Taiwanese population. Lipids Health Dis 2019; 18:111. [PMID: 31077211 PMCID: PMC6511151 DOI: 10.1186/s12944-019-1057-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 04/24/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Hepatic lipase (HL, encoded by LIPC) is a glycoprotein primarily synthesized and secreted by hepatocytes. Previous studies had demonstrated that HL is crucial for reverse cholesterol transport and affects the metabolism, composition, and level of several lipoproteins. In current study, we investigated the association of LIPC (Lipase C, Hepatic Type) variants with circulating and urinary biomarker levels by using subgroup and mediation analyses. METHODS A total of 572 participants from Taiwan were genotyped for three LIPC single nucleotide polymorphisms (SNPs) by using TaqMan assay. Fasting levels of glucose, lipid profile, inflammation markers, urine creatinine and 8-hydroxy deoxyguanosine (8-OHdG) were measured. The chi-square test, 2-sample t test and Analysis of variance (ANOVA) were used to examine differences among variables and genotype frequencies. RESULTS SNPs rs2043085 and rs1532085 were significantly associated with urinary 8-OHdG levels, whereas all three SNPs were more significantly associated with Triglycerides (TG) or HDL-cholesterol (HDL-C) levels after additional adjustment for HDL-C or TG levels, respectively. Subgroup analyses revealed that the association of the LIPC SNPs with the levels of serum TG, HDL-C, and urinary 8-OHdG were predominantly observed in the men but not in the women. Differential associations of the LIPC SNPs with various lipid levels were observed in participants with different adiposity statuses. Mediation analyses indicated that TG levels acted as a suppressor masking the association of the LIPC genotypes with HDL-C levels, particularly in the men (Sobel test, all P < 0.01). CONCLUSION Our data revealed that interaction and suppression effects mediated the pleiotropic association of the LIPC variants. The effects of the LIPC SNPs depended on sex, adiposity status, and TG levels. Thus, our findings can provide a method for identifying high-risk populations of cardiovascular diseases for clinical diagnosis.
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Affiliation(s)
- Ming-Sheng Teng
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Semon Wu
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan.,Department of Life Science, Chinese Culture University, Taipei, Taiwan
| | - Lung-An Hsu
- The First Cardiovascular Division, Department of Internal Medicine, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - I-Shiang Tzeng
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Hsin-Hua Chou
- The Division of Cardiology, Department of Internal Medicine and Cardiovascular Center, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Cheng-Wen Su
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan
| | - Yu-Lin Ko
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan. .,The Division of Cardiology, Department of Internal Medicine and Cardiovascular Center, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei city, Taiwan. .,School of Medicine, Tzu Chi University, Hualien, Taiwan.
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32
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Abstract
After more than 10 years of accumulated efforts, genome-wide association studies (GWAS) have led to many findings, most of which have been deposited into the GWAS Catalog. Between GWAS's inception and March 2017, the GWAS Catalog has collected 2429 studies, 1818 phenotypes, and 28,462 associated SNPs. We reclassified the psychology-related phenotypes into 217 reclassified phenotypes, which accounted for 514 studies and 7052 SNPs. In total, 1223 of the SNPs reached genome-wide significance. Of these, 147 were replicated for the same psychological trait in different studies. Another 305 SNPs were replicated within one original study. The SNPs rs2075650 and rs4420638 were linked to the most replications within a single reclassified phenotype or very similar reclassified phenotypes; both were associated with Alzheimer's disease (AD). Schizophrenia was associated with 74 within-phenotype SNPs reported in independents studies. Alzheimer's disease and schizophrenia were both linked to some physical phenotypes, including cholesterol and body mass index, through common GWAS signals. Alzheimer's disease also shared risk SNPs with age-related phenotypes such as age-related macular degeneration and longevity. Smoking-related SNPs were linked to lung cancer and respiratory function. Alcohol-related SNPs were associated with cardiovascular and digestive system phenotypes and disorders. Two separate studies also identified a shared risk SNP for bipolar disorder and educational attainment. This review revealed a list of reproducible SNPs worthy of future functional investigation. Additionally, by identifying SNPs associated with multiple phenotypes, we illustrated the importance of studying the relationships among phenotypes to resolve the nature of their causal links. The insights within this review will hopefully pave the way for future evidence-based genetic studies.
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33
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Zhang QH, Yin RX, Chen WX, Cao XL, Wu JZ. TRIB1 and TRPS1 variants, G × G and G × E interactions on serum lipid levels, the risk of coronary heart disease and ischemic stroke. Sci Rep 2019; 9:2376. [PMID: 30787327 PMCID: PMC6382757 DOI: 10.1038/s41598-019-38765-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 01/09/2019] [Indexed: 02/07/2023] Open
Abstract
This study aimed to assess the association of the tribbles pseudokinase 1 (TRIB1) and transcriptional repressor GATA binding 1 (TRPS1) single nucleotide polymorphisms (SNPs) and the gene-gene (G × G) and gene-environment (G × E) interactions with serum lipid levels, the risk of coronary heart disease (CHD) and ischemic stroke (IS) in the Guangxi Han population. Genotyping of the rs2954029, rs2980880, rs10808546, rs231150, rs2737229 and rs10505248 SNPs was performed in 625 controls and 1146 unrelated patients (CHD, 593 and IS, 553). The genotypic and allelic frequencies of some SNPs were different between controls and patients (CHD, rs2954029 and rs231150; IS, rs2954029 and rs2980880; P < 0.05-0.01). Two SNPs were associated with increased risk of CHD (rs2954029 and rs231150) and IS (rs2954029) in different genetic models. Several SNPs in controls were associated with total cholesterol (rs2954029, rs2980880 and rs2737229), triglyceride (rs2954029 and rs10808546), low-density lipoprotein cholesterol (rs2954029), high-density lipoprotein cholesterol (rs2980880 and rs231150) and apolipoprotein A1 (rs2737229) levels. The rs2954029TA/AA-age (>60 year) interaction increased the risk of CHD, whereas the rs10808546CT/TT-drinking interaction decreased the risk of IS. The rs2954029A-rs2980880C-rs10808546C haplotype was associated with increased risk of CHD and IS. The rs2954029A-rs2980880T-rs10808546C haplotype was associated with increased risk of CHD. The rs2954029-rs231150 interactions had an increased risk of both CHD and IS. These results suggest that several TRIB1 and TRPS1 SNPs were associated with dyslipidemia and increased risk of CHD and IS in our study population. The G × G and G × E interactions on serum lipid levels, and the risk of CHD and IS were also observed.
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Affiliation(s)
- Qing-Hui Zhang
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Rui-Xing Yin
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.
| | - Wu-Xian Chen
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Xiao-Li Cao
- Department of Neurology, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
| | - Jin-Zhen Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, The First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China
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34
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Kilpeläinen TO, Bentley AR, Noordam R, Sung YJ, Schwander K, Winkler TW, Jakupović H, Chasman DI, Manning A, Ntalla I, Aschard H, Brown MR, de las Fuentes L, Franceschini N, Guo X, Vojinovic D, Aslibekyan S, Feitosa MF, Kho M, Musani SK, Richard M, Wang H, Wang Z, Bartz TM, Bielak LF, Campbell A, Dorajoo R, Fisher V, Hartwig FP, Horimoto ARVR, Li C, Lohman KK, Marten J, Sim X, Smith AV, Tajuddin SM, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Evangelou E, Gao C, Graff M, Harris SE, He M, Hsu FC, Jackson AU, Zhao JH, Kraja AT, Kühnel B, Laguzzi F, Lyytikäinen LP, Nolte IM, Rauramaa R, Riaz M, Robino A, Rueedi R, Stringham HM, Takeuchi F, van der Most PJ, Varga TV, Verweij N, Ware EB, Wen W, Li X, Yanek LR, Amin N, Arnett DK, Boerwinkle E, Brumat M, Cade B, Canouil M, Chen YDI, Concas MP, Connell J, de Mutsert R, de Silva HJ, de Vries PS, Demirkan A, Ding J, Eaton CB, Faul JD, Friedlander Y, Gabriel KP, Ghanbari M, Giulianini F, Gu CC, Gu D, Harris TB, He J, Heikkinen S, Heng CK, Hunt SC, Ikram MA, Jonas JB, Koh WP, Komulainen P, Krieger JE, Kritchevsky SB, Kutalik Z, Kuusisto J, Langefeld CD, Langenberg C, Launer LJ, Leander K, Lemaitre RN, Lewis CE, Liang J, Liu J, Mägi R, Manichaikul A, Meitinger T, Metspalu A, Milaneschi Y, Mohlke KL, Mosley TH, Murray AD, Nalls MA, Nang EEK, Nelson CP, Nona S, Norris JM, Nwuba CV, O'Connell J, Palmer ND, Papanicolau GJ, Pazoki R, Pedersen NL, Peters A, Peyser PA, Polasek O, Porteous DJ, Poveda A, Raitakari OT, Rich SS, Risch N, Robinson JG, Rose LM, Rudan I, Schreiner PJ, Scott RA, Sidney SS, Sims M, Smith JA, Snieder H, Sofer T, Starr JM, Sternfeld B, Strauch K, Tang H, Taylor KD, Tsai MY, Tuomilehto J, Uitterlinden AG, van der Ende MY, van Heemst D, Voortman T, Waldenberger M, Wennberg P, Wilson G, Xiang YB, Yao J, Yu C, Yuan JM, Zhao W, Zonderman AB, Becker DM, Boehnke M, Bowden DW, de Faire U, Deary IJ, Elliott P, Esko T, Freedman BI, Froguel P, Gasparini P, Gieger C, Kato N, Laakso M, Lakka TA, Lehtimäki T, Magnusson PKE, Oldehinkel AJ, Penninx BWJH, Samani NJ, Shu XO, van der Harst P, Van Vliet-Ostaptchouk JV, Vollenweider P, Wagenknecht LE, Wang YX, Wareham NJ, Weir DR, Wu T, Zheng W, Zhu X, Evans MK, Franks PW, Gudnason V, Hayward C, Horta BL, Kelly TN, Liu Y, North KE, Pereira AC, Ridker PM, Tai ES, van Dam RM, Fox ER, Kardia SLR, Liu CT, Mook-Kanamori DO, Province MA, Redline S, van Duijn CM, Rotter JI, Kooperberg CB, Gauderman WJ, Psaty BM, Rice K, Munroe PB, Fornage M, Cupples LA, Rotimi CN, Morrison AC, Rao DC, Loos RJF. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. Nat Commun 2019; 10:376. [PMID: 30670697 PMCID: PMC6342931 DOI: 10.1038/s41467-018-08008-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 12/07/2018] [Indexed: 11/08/2022] Open
Abstract
Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.
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Affiliation(s)
- Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.
- Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Raymond Noordam
- Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, 63110, MO, USA
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, 63110, MO, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, 93051, Germany
| | - Hermina Jakupović
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Daniel I Chasman
- Preventive Medicine, Brigham and Women's Hospital, Boston, 02215, MA, USA
- Harvard Medical School, Boston, 02131, MA, USA
| | - Alisa Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, 02114, MA, USA
- Department of Medicine, Harvard Medical School, Boston, 02115, MA, USA
| | - Ioanna Ntalla
- Clinical Pharmacology, William Harvey Research Instititute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, 02115, MA, USA
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, 75015, France
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Lisa de las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, 63110, MO, USA
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, 63110, MO, USA
| | - Nora Franceschini
- Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, 27514, NC, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, 90502, CA, USA
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, 35294, AL, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, 63108, MO, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Solomon K Musani
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, 39213, MS, USA
| | - Melissa Richard
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, 02115, MA, USA
| | - Zhe Wang
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, 98101, WA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Archie Campbell
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, 138672, Singapore
| | - Virginia Fisher
- Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA
| | - Fernando P Hartwig
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, 96020220, RS, Brazil
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Andrea R V R Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, 01246903, SP, Brazil
| | - Changwei Li
- Epidemiology and Biostatistics, University of Giorgia at Athens College of Public Health, Athens, 30602, GA, USA
| | - Kurt K Lohman
- Public Health Sciences, Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, 27157, NC, USA
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
| | - Albert V Smith
- Icelandic Heart Association, 201, Kopavogur, Iceland
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Salman M Tajuddin
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, 21224, MD, USA
| | - Maris Alver
- Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
| | - Marzyeh Amini
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Mathilde Boissel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, 59000, France
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, 17177, Sweden
| | - Jasmin Divers
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece
| | - Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Mariaelisa Graff
- Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, 27514, NC, USA
| | - Sarah E Harris
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Fang-Chi Hsu
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Aldi T Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, 63108, MO, USA
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Federica Laguzzi
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland
| | - Muhammad Riaz
- College of Medicine, Biological Sciences and Psychology, Health Sciences, The Infant Mortality and Morbidity Studies (TIMMS), Leicester, LE1 7RH, UK
| | - Antonietta Robino
- Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, 34137, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, 1015, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, 1628655, Japan
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Tibor V Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, 20502, Sweden
| | - Niek Verweij
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, 9700 RB, The Netherlands
| | - Erin B Ware
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, 48104, MI, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, 37203, TN, USA
| | - Xiaoyin Li
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lisa R Yanek
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - Donna K Arnett
- Dean's Office, University of Kentucky College of Public Health, Lexington, 40536, KY, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030, TX, USA
| | - Marco Brumat
- Department of Medical Sciences, University of Trieste, Trieste, 34137, Italy
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, 02115, MA, USA
| | - Mickaël Canouil
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, 59000, France
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, 90502, CA, USA
| | - Maria Pina Concas
- Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, 34137, Italy
| | - John Connell
- Ninewells Hospital & Medical School, University of Dundee, Dundee, DD1 9SY, Scotland, UK
| | - Renée de Mutsert
- Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300 RC, Netherlands
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, 11600, Sri Lanka
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Ayşe Demirkan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - Jingzhong Ding
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Charles B Eaton
- Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, 02860, RI, USA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, 48104, MI, USA
| | - Yechiel Friedlander
- Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, 91120, Israel
| | - Kelley P Gabriel
- Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Austin, Austin, 78712, TX, USA
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, 91778-99191, Iran
| | - Franco Giulianini
- Preventive Medicine, Brigham and Women's Hospital, Boston, 02215, MA, USA
| | - Chi Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, 63110, MO, USA
| | - Dongfeng Gu
- Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100006, China
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Jiang He
- Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, 70112, LA, USA
- Medicine, Tulane University School of Medicine, New Orleans, 70112, LA, USA
| | - Sami Heikkinen
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, 70211, Finland
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, 119228, Singapore
| | - Steven C Hunt
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, 84132, UT, USA
- Department of Genetic Medicine, Weill Cornell Medicine, Doha, 24144, Qatar
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, 3015 GD, The Netherlands
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, 68167, Germany
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Pirjo Komulainen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland
| | - Jose E Krieger
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, 01246903, SP, Brazil
| | - Stephen B Kritchevsky
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, 1010, Switzerland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
| | - Carl D Langefeld
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Karin Leander
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, 98101, WA, USA
| | - Cora E Lewis
- Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, School of Medicine, Birmingham, 35294, AL, USA
| | - Jingjing Liang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, 138672, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, 22908, VA, USA
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Human Genetics, Technische Universität München, Munich, 80333, Germany
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, 1081 HV, The Netherlands
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, 27514, NC, USA
| | - Thomas H Mosley
- Geriatrics, Medicine, University of Mississippi, Jackson, 39216, MS, USA
| | - Alison D Murray
- The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, AB25 2ZD, UK
| | - Mike A Nalls
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, 20892, MD, USA
- Data Tecnica International, Glen Echo, 20812, MD, USA
| | - Ei-Ei Khaing Nang
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, LE3 9PQ, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QD, UK
| | - Sotoodehnia Nona
- Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, 98101, WA, USA
| | - Jill M Norris
- Department of Epidemiology, University of Colorado Denver, Aurora, 80045, CO, USA
| | - Chiamaka Vivian Nwuba
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Jeff O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, 21201, MD, USA
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, 21201, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - George J Papanicolau
- Epidemiology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Raha Pazoki
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, 17177, Sweden
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, 85764, Germany
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Ozren Polasek
- Department of Public Health, Department of Medicine, University of Split, Split, 21000, Croatia
- Psychiatric Hospital "Sveti Ivan", Zagreb, 10000, Croatia
- Gen-Info Ltd., 10000, Zagreb, Croatia
| | - David J Porteous
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Alaitz Poveda
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, 20502, Sweden
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20521, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, 22908, VA, USA
| | - Neil Risch
- Institute for Human Genetics, Department of Epidemiology and Biostatistics, University of California, San Francisco, 94143, CA, USA
| | - Jennifer G Robinson
- Department of Epidemiology and Medicine, University of Iowa, Iowa City, 52242, IA, USA
| | - Lynda M Rose
- Preventive Medicine, Brigham and Women's Hospital, Boston, 02215, MA, USA
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH16 4UX, UK
| | - Pamela J Schreiner
- Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, 55454, MN, USA
| | - Robert A Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Stephen S Sidney
- Kaiser Permanente Washington, Health Research Institute, Seattle, 98101, WA, USA
| | - Mario Sims
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, 39213, MS, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, 48104, MI, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, 02115, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, 02115, MA, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Barbara Sternfeld
- Kaiser Permanente Washington, Health Research Institute, Seattle, 98101, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Medical Informatics Biometry and Epidemiology, Ludwig-Maximilians-Universitat Munchen, Munich, 81377, Germany
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, 94305, CA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, 90502, CA, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, 55455, MN, USA
| | - Jaakko Tuomilehto
- Public Health Solutions, National Institute for Health and Welfare, Helsinki, 00271, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - M Yldau van der Ende
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, 9700 RB, The Netherlands
| | - Diana van Heemst
- Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Patrik Wennberg
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, 90185, Västerbotten, Sweden
| | - Gregory Wilson
- Jackson Heart Study, School of Public Health, Jackson State University, Jackson, 39213, MS, USA
| | - Yong-Bing Xiang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200000, China
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, 90502, CA, USA
| | - Caizheng Yu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, 15261, PA, USA
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer, University of Pittsburgh, Pittsburgh, 15232, PA, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Alan B Zonderman
- Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, 21224, MD, USA
| | - Diane M Becker
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Ulf de Faire
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Psychology, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, 02142, MA, USA
| | - Barry I Freedman
- Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Philippe Froguel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, 59000, France
- Department of Genomics of Common Disease, Imperial College London, London, W12 0NN, UK
| | - Paolo Gasparini
- Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", Trieste, 34137, Italy
- Department of Medical Sciences, University of Trieste, Trieste, 34137, Italy
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, 85764, Germany
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, 1628655, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, 70210, Finland
| | - Timo A Lakka
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, 70100, Finland
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, 70211, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, 70210, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, 33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, 17177, Sweden
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, 1081 HV, The Netherlands
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, LE3 9PQ, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, LE3 9QD, UK
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, 37203, TN, USA
| | - Pim van der Harst
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, 9700 RB, The Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, The Netherlands
- Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, 1105 AZ, The Netherlands
| | - Jana V Van Vliet-Ostaptchouk
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, 9713 GZ, The Netherlands
| | - Peter Vollenweider
- Internal Medicine, Department of Medicine, Lausanne University Hospital, Lausanne, 1011, Switzerland
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, 27157, NC, USA
| | - Ya X Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | | | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, 48104, MI, USA
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, 37203, TN, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Michele K Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, 21224, MD, USA
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, 20502, Sweden
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, 90185, Västerbotten, Sweden
- Harvard T. H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, 02115, MA, USA
- OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7LE, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, 201, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, 101, Iceland
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Bernardo L Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, 96020220, RS, Brazil
| | - Tanika N Kelly
- Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, 70112, LA, USA
| | - Yongmei Liu
- Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, 27157, NC, USA
| | - Kari E North
- Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, 27514, NC, USA
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, 01246903, SP, Brazil
| | - Paul M Ridker
- Preventive Medicine, Brigham and Women's Hospital, Boston, 02215, MA, USA
- Harvard Medical School, Boston, 02131, MA, USA
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, 169857, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, 117549, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Ervin R Fox
- Cardiology, Medicine, University of Mississippi Medical Center, Jackson, 39216, MS, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Ching-Ti Liu
- Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA
| | - Dennis O Mook-Kanamori
- Clinical Epidemiology, Leiden University Medical Center, Leiden, 2300 RC, Netherlands
- Public Health and Primary Care, Leiden University Medical Center, Leiden, 2300 RC, The Netherlands
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, 63108, MO, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, 02115, MA, USA
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, 90502, CA, USA
| | - Charles B Kooperberg
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, 98109, WA, USA
| | - W James Gauderman
- Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, 90032, CA, USA
| | - Bruce M Psaty
- Kaiser Permanente Washington, Health Research Institute, Seattle, 98101, WA, USA
- Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, 98101, WA, USA
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, 98105, WA, USA
| | - Patricia B Munroe
- Clinical Pharmacology, William Harvey Research Instititute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
- NIHR Barts Cardiovascular Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - L Adrienne Cupples
- Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA
- NHLBI Framingham Heart Study, Framingham, 01702, MA, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, 63110, MO, USA.
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, 10029, NY, USA.
- Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, 10029, NY, USA.
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Pikó P, Fiatal S, Kósa Z, Sándor J, Ádány R. Generalizability and applicability of results obtained from populations of European descent regarding the effect direction and size of HDL-C level-associated genetic variants to the Hungarian general and Roma populations. Gene 2018; 686:187-193. [PMID: 30468910 DOI: 10.1016/j.gene.2018.11.067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/28/2018] [Accepted: 11/19/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Large-scale association studies that mainly involve European populations identified many genetic loci related to high-density lipoprotein cholesterol (HDL-C) levels, one of the most important indicators of the risk for cardiovascular diseases. The question with intense speculation of whether the effect estimates obtained from European populations for different HDL-C level-related SNPs are applicable to the Roma ethnicity, the largest minority group in Europe with a South Asian origin, was addressed in the present study. DESIGN The associations between 21 SNPs (in the genes LIPC(G), CETP, GALNT2, HMGCP, ABCA1, KCTD10 and WWOX) and HDL-C levels were examined separately in adults of the Hungarian general (N = 1542) and Roma (N = 646) populations by linear regression. Individual effects (direction and size) of single SNPs on HDL-C levels were computed and compared between the study groups and with data published in the literature. RESULTS Significant associations between SNPs and HDL-C levels were more frequently found in general subjects than in Roma subjects (11 SNPs in general vs. 4 SNPs in Roma). The CETP gene variants rs1532624, rs708272 and rs7499892 consistently showed significant associations with HDL-C levels across the study groups (p ˂ 0.05), indicating a possible causal variant(s) in this region. Although nominally significant differences in effect size were found for three SNPs (rs693 in gene APOB, rs9989419 in gene CETP, and rs2548861 in gene WWOX) by comparing the general and Roma populations, most of these SNPs did not have a significant effect on HDL-C levels. The β coefficients for SNPs in the Roma population were found to be identical both in direction and magnitude to the effect obtained previously in large-scale studies on European populations. CONCLUSIONS The effect of the vast majority of the SNPs on HDL-C levels could be replicated in the Hungarian general and Roma populations, which indicates that the effect size measurements obtained from the literature can be used for risk estimation for both populations.
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Affiliation(s)
- Péter Pikó
- MTA-DE Public Health Research Group of the Hungarian Academy of Sciences, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary
| | - Szilvia Fiatal
- Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; WHO Collaborating Centre on Vulnerability and Health, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary
| | - Zsigmond Kósa
- Department of Health Visitor Methodology and Public Health, Faculty of Health, University of Debrecen, Nyíregyháza 4400, Hungary
| | - János Sándor
- Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; WHO Collaborating Centre on Vulnerability and Health, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary
| | - Róza Ádány
- MTA-DE Public Health Research Group of the Hungarian Academy of Sciences, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary; WHO Collaborating Centre on Vulnerability and Health, Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen 4028, Hungary.
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Liu W, Cui Z, Xu P, Han H, Zhu J. Conditional GWAS revealing genetic impacts of lifestyle behaviors on low-density lipoprotein (LDL). Comput Biol Chem 2018; 78:497-503. [PMID: 30473251 DOI: 10.1016/j.compbiolchem.2018.11.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 11/16/2018] [Indexed: 10/27/2022]
Abstract
BACKGROUND Accumulation of LDL cholesterol (LDL-c) within artery walls is strongly associated with the initiation and progression of atherosclerosis development. This complex trait is affected by multifactor involving polygenes, environments, and their interactions. Uncovering genetic architecture of LDL may help to increase the understanding of the genetic mechanism of cardiovascular diseases. METHODS We used a genetic model to analyze genetic effects including additive, dominance, epistasis, and ethnic interactions for data from the Multi-Ethnic Study of Atherosclerosis (MESA). Three lifestyle behaviors (reading, intentional exercising, smoking) were used as cofactor in conditional models. RESULTS We identified 156 genetic effects of 10 quantitative trait SNPs (QTSs) in base model and three conditional models. The total estimated heritability of these genetic effects was approximately 72.88% in the base model. Five genes (CELSR2, MARK2, ADAMTS12, PFDN4, and MAGI2) have biological functions related to LDL. CONCLUSIONS Compared with the based model LDL, the results in three conditional models revealed that intentional exercising and smoking could have impacts for causing and suppressing some of genetic effects and influence the levels of LDL. Furthermore, these two lifestyles could have different genetic effects for each ethnic group on a specific QTS. As most of the heritability in based model LDL and conditional model LDL|Smk was contributed from epistasis effects, our result indicated that epistasis effects played important roles in determining LDL levels. Our study provided useful insight into the biological mechanisms underlying regulation of LDL and might help in the discovery of novel therapeutic targets for cardiovascular disease.
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Affiliation(s)
- Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Zhendong Cui
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China
| | - Peng Xu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China
| | - Henrry Han
- Department of Computer and Information Science, Fordham University, New York, NY, 10458, USA
| | - Jun Zhu
- Institute of Bioinformatics, Zhejiang University, Hangzhou, 310058, China.
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Profile of Dr. Dongfeng Gu. SCIENCE CHINA. LIFE SCIENCES 2018; 61:501-503. [PMID: 29564600 DOI: 10.1007/s11427-018-9282-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
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Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, Kvale MN, Kwok PY, Schaefer C, Krauss RM, Iribarren C, Risch N. A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet 2018; 50:401-413. [PMID: 29507422 PMCID: PMC5942247 DOI: 10.1038/s41588-018-0064-5] [Citation(s) in RCA: 191] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 01/19/2018] [Indexed: 12/16/2022]
Abstract
A genome-wide association study of 94,674 multi-ethnic Kaiser Permanente members utilizing 478,866 longitudinal untreated serum lipid electronic-health-record-derived measurements (EHRs) empowered multiple novel findings: 121 new SNP associations (46 primary, 15 conditional, 60 in meta-analysis with Global Lipids Genetic Consortium); increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater in females for LDL, HDL, TC, the opposite for TG); differences in variance explained amongst non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistasis, with strong evidence for both at ABOxFUT2 for LDL; and eQTL tissue-enrichment implicating the liver, adipose, and pancreas. Utilizing EHR pharmacy data, both LDL and TG genetic risk scores (477 SNPs) were strongly predictive of age-at-initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying novel genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.
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Affiliation(s)
- Thomas J Hoffmann
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
| | | | - Tanushree Haldar
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Dilrini K Ranatunga
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Marisa W Medina
- Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Mark N Kvale
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Pui-Yan Kwok
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Catherine Schaefer
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Ronald M Krauss
- Children's Hospital Oakland Research Institute, Oakland, CA, USA
| | - Carlos Iribarren
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA
| | - Neil Risch
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA. .,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. .,Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA.
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Hovsepian S, Javanmard SH, Mansourian M, Hashemipour M, Tajadini M, Kelishadi R. Lipid regulatory genes polymorphism in children with and without obesity and cardiometabolic risk factors: The CASPIAN-III study. JOURNAL OF RESEARCH IN MEDICAL SCIENCES 2018. [PMID: 29531563 PMCID: PMC5842446 DOI: 10.4103/jrms.jrms_911_17] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Genetically, predisposed children are considered as at-risk individuals for cardiovascular disease. In this study, we aimed to compare the frequency of four-lipid regulatory polymorphism in obese and normal-weight children with and without cardiometabolic risk factors. Materials and Methods: In this nested case–control study, 600 samples of four groups of participants consisted of those with normal weight with and without cardiometabolic risk factors and obese with and without cardiometabolic risk factors. Allelic and genotypic frequencies of GCKR (rs780094), GCKR (rs1260333), MLXIPL (rs3812316), and FADS (rs174547) polymorphisms were compared in the four studied groups. Results: Data of 528 samples were complete and included in this study. The mean (standard deviation) age of participants was 15.01 (2.21) years. Frequency of tt allele (minor allele) of GCKR (rs1260333) polymorphism was significantly lower in normal weight metabolically healthy participants than metabolically unhealthy normal weight (MUHNW) and obese children with and without cardiometabolic risk factor (P = 0.01). Frequency of ga allele of GCKR (rs780094) polymorphism was significantly higher in normal weight children with cardiometabolic risk factor than in their obese counterparts with cardiometabolic risk factor (P = 0.04). Frequency of cg and gg alleles (minor type) of MLXIPL (rs3812316) polymorphism in normal weight metabolically healthy participants was significantly higher than MUHNW (P = 0.04) and metabolically healthy obese children (P = 0.04). Conclusion: The findings of our study indicated that the minor allele of GCKR (rs1260333) single nucleotide polymorphisms (SNPs) could have pathogenic effect for obesity and cardiometabolic risk factors. Ga allele of GCKR (rs780094) SNPs had a protective effect on obesity. Minor alleles of MLXIPL (rs3812316) could have a protective effect for obesity and cardiometabolic risk factors.
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Affiliation(s)
- Silva Hovsepian
- Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Emam Hossein Children's Hospital, Isfahan, Iran
| | - Shaghayegh Haghjooy Javanmard
- Applied Physiology Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Marjan Mansourian
- Department of Biostatistics and Epidemiology, School of Health, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahin Hashemipour
- Isfahan Endocrine and Metabolism Research Center, Department of Pediatrics, Emam Hossein Children's Hospital, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohamadhasan Tajadini
- Applied Physiology Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Roya Kelishadi
- Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
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Andaleon A, Mogil LS, Wheeler HE. Gene-based association study for lipid traits in diverse cohorts implicates BACE1 and SIDT2 regulation in triglyceride levels. PeerJ 2018; 6:e4314. [PMID: 29404214 PMCID: PMC5793713 DOI: 10.7717/peerj.4314] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 01/11/2018] [Indexed: 11/29/2022] Open
Abstract
Plasma lipid levels are risk factors for cardiovascular disease, a leading cause of death worldwide. While many studies have been conducted on lipid genetics, they mainly focus on Europeans and thus their transferability to diverse populations is unclear. We performed SNP- and gene-level genome-wide association studies (GWAS) of four lipid traits in cohorts from Nigeria and the Philippines and compared them to the results of larger, predominantly European meta-analyses. Two previously implicated loci met genome-wide significance in our SNP-level GWAS in the Nigerian cohort, rs34065661 in CETP associated with HDL cholesterol (P = 9.0 × 10-10) and rs1065853 upstream of APOE associated with LDL cholesterol (P = 6.6 × 10-9). The top SNP in the Filipino cohort associated with triglyceride levels (rs662799; P = 2.7 × 10-16) and has been previously implicated in other East Asian studies. While this SNP is located directly upstream of well known APOA5, we show it may also be involved in the regulation of BACE1 and SIDT2. Our gene-based association analysis, PrediXcan, revealed decreased expression of BACE1 and decreased expression of SIDT2 in several tissues, all driven by rs662799, significantly associate with increased triglyceride levels in Filipinos (FDR <0.1). In addition, our PrediXcan analysis implicated gene regulation as the mechanism underlying the associations of many other previously discovered lipid loci. Our novel BACE1 and SIDT2 findings were confirmed using summary statistics from the Global Lipids Genetic Consortium (GLGC) meta-GWAS.
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Affiliation(s)
- Angela Andaleon
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Lauren S. Mogil
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
- Department of Computer Science, Loyola University Chicago, Chicago, IL, United States of America
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, United States of America
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Teng MS, Wu S, Er LK, Hsu LA, Chou HH, Ko YL. LIPC variants as genetic determinants of adiposity status, visceral adiposity indicators, and triglyceride-glucose (TyG) index-related parameters mediated by serum triglyceride levels. Diabetol Metab Syndr 2018; 10:79. [PMID: 30410583 PMCID: PMC6218991 DOI: 10.1186/s13098-018-0383-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/01/2018] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Visceral adiposity indicators and the product of triglyceride and fasting plasma glucose (TyG) index-related parameters are effective surrogate markers for insulin resistance (IR) and are predictors of metabolic syndrome and diabetes mellitus. However, their genetic determinants have not been previously reported. Pleiotropic associations of LIPC variants have been observed in lipid profiles and atherosclerotic cardiovascular diseases. We aimed to investigate LIPC polymorphisms as the genetic determinants of adiposity status, visceral adiposity indicators and TyG index-related parameters. METHODS A total of 592 participants from Taiwan were genotyped for three LIPC single nucleotide polymorphisms (SNPs). RESULTS The LIPC SNPs rs2043085 and rs1532085 were significantly associated with body mass index (BMI), waist circumference (WC), lipid accumulation product, visceral adiposity index, and TyG index-related parameters [including the TyG index, TyG with adiposity status (TyG-BMI), and TyG-WC index], whereas the rs1800588 SNP was only significantly associated with the TyG index. The associations became nonsignificant after further adjustment for serum TG levels. No significant association was observed between any the studied LIPC SNPs and IR status. CONCLUSION Our data revealed a pleiotropic association between the LIPC variants and visceral adiposity indicators and TyG index-related parameters, which are mediated by serum TG levels.
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Affiliation(s)
- Ming-Sheng Teng
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 23142 Taiwan
| | - Semon Wu
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 23142 Taiwan
- Department of Life Science, Chinese Culture University, Taipei, 11114 Taiwan
| | - Leay-Kiaw Er
- The Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 23142 Taiwan
- School of Medicine, Tzu Chi University, Hualien, 97071 Taiwan
| | - Lung-An Hsu
- First Cardiovascular Division, Department of Internal Medicine, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, 33305 Taiwan
| | - Hsin-Hua Chou
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 23142 Taiwan
| | - Yu-Lin Ko
- Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 23142 Taiwan
- School of Medicine, Tzu Chi University, Hualien, 97071 Taiwan
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, 23142 Taiwan
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Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat Genet 2017; 49:1722-1730. [PMID: 29083407 PMCID: PMC5899829 DOI: 10.1038/ng.3978] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 09/26/2017] [Indexed: 12/13/2022]
Abstract
Most genome-wide association studies have been conducted in European individuals, even though most genetic variation in humans is seen only in non-European samples. To search for novel loci associated with blood lipid levels and clarify the mechanism of action at previously identified lipid loci, we examined protein-coding genetic variants in 47,532 East Asian individuals using an exome array. We identified 255 variants at 41 loci reaching chip-wide significance, including 3 novel loci and 14 East Asian-specific coding variant associations. After meta-analysis with > 300,000 European samples, we identified an additional 9 novel loci. The same 16 genes were identified by the protein-altering variants in both East Asians and Europeans, likely pointing to the functional genes. Our data demonstrate that most of the low-frequency or rare coding variants associated with lipids are population-specific, and that examining genomic data across diverse ancestries may facilitate the identification of functional genes at associated loci.
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Richardson TG, Zheng J, Davey Smith G, Timpson NJ, Gaunt TR, Relton CL, Hemani G. Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk. Am J Hum Genet 2017; 101:590-602. [PMID: 28985495 PMCID: PMC5630190 DOI: 10.1016/j.ajhg.2017.09.003] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 09/06/2017] [Indexed: 01/10/2023] Open
Abstract
The extent to which genetic influences on cardiovascular disease risk are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified ten genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits after multiple-testing correction. Bivariate fine mapping provided evidence that the individual variants responsible for the observed effects on cardiovascular traits at the ADCY3 and ADIPOQ loci were potentially mediated through changes in DNA methylation, although we highlight that we are unable to reliably separate causality from horizontal pleiotropy. Estimates of causal effects were replicated with results from large-scale consortia. Genetic variants and CpG sites identified in this study were enriched for histone mark peaks in relevant tissue types and gene promoter regions. Integrating our results with expression quantitative trait loci data, we provide evidence that variation at these regulatory regions is likely to also influence gene expression levels at these loci.
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK.
| | - Jie Zheng
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
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Haerian BS, Haerian MS, Roohi A, Mehrad-Majd H. ABCA1 genetic polymorphisms and type 2 diabetes mellitus and its complications. Meta Gene 2017. [DOI: 10.1016/j.mgene.2017.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Identification of eight genetic variants as novel determinants of dyslipidemia in Japanese by exome-wide association studies. Oncotarget 2017; 8:38950-38961. [PMID: 28473662 PMCID: PMC5503585 DOI: 10.18632/oncotarget.17159] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 04/04/2017] [Indexed: 11/29/2022] Open
Abstract
We have performed exome-wide association studies to identify single nucleotide polymorphisms that influence serum concentrations of triglycerides, high density lipoprotein (HDL)–cholesterol, or low density lipoprotein (LDL)–cholesterol or confer susceptibility to hypertriglyceridemia, hypo–HDL-cholesterolemia, or hyper–LDL-cholesterolemia in Japanese. Exome-wide association studies for serum triglycerides (13,414 subjects), HDL-cholesterol (14,119 subjects), LDL-cholesterol (13,577 subjects), hypertriglyceridemia (4742 cases, 8672 controls), hypo–HDL-cholesterolemia (2646 cases, 11,473 controls), and hyper–LDL-cholesterolemia (4489 cases, 9088 controls) were performed with HumanExome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays. Twenty-four, 69, or 32 loci were significantly (P < 1.21 × 10−6) associated with serum triglycerides, HDL-cholesterol, or LDL-cholesterol, respectively, with 13, 16, or 9 of these loci having previously been associated with triglyceride-, HDL-cholesterol–, or LDL-cholesterol–related traits, respectively. Two single nucleotide polymorphisms (rs10790162, rs7350481) were significantly related to both serum triglycerides and hypertriglyceridemia; three polymorphisms (rs146515657, rs147317864, rs12229654) were significantly related to both serum HDL-cholesterol and hypo–HDL-cholesterolemia; and six polymorphisms (rs2853969, rs7771335, rs2071653, rs2269704, rs2269703, rs2269702) were significantly related to both serum LDL-cholesterol and hyper–LDL-cholesterolemia. Among polymorphisms identified in the present study, two polymorphisms (rs146515657, rs147317864) may be novel determinants of hypo–HDL-cholesterolemia, and six polymorphisms (rs2853969, rs7771335, rs2071653, rs2269704, rs2269703, rs2269702) may be new determinants of hyper–LDL-cholesterolemia. In addition, 12, 61, 23, or 3 polymorphisms may be new determinants of the serum triglyceride, HDL-cholesterol, or LDL-cholesterol concentrations or of hyper–LDL-cholesterolemia, respectively.
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Lu X, Li J, Li H, Chen Y, Wang L, He M, Wang Y, Sun L, Hu Y, Huang J, Wang F, Liu X, Chen S, Yu K, Yang X, Mo Z, Lin X, Wu T, Gu D. Coding-sequence variants are associated with blood lipid levels in 14,473 Chinese. Hum Mol Genet 2016; 25:4107-4116. [PMID: 27516387 DOI: 10.1093/hmg/ddw261] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 07/08/2016] [Accepted: 07/26/2016] [Indexed: 01/09/2023] Open
Abstract
Previously identified common variants explain only a small fraction of the trait heritability and at most loci the identities of the underlying causal genes and their functional variants still remain unknown. To identify the low-frequency and rare coding variants that influence lipid levels, we conducted a meta-analysis of exome-wide association studies in 14,473 Chinese subjects, followed by a joint analysis with 1000 genomes imputed data from 6,534 samples. We replicated 24 previously reported lipid loci with exome-wide significance (P < 3.3 × 10 - 7), including fourteen coding variants at ten confirmed lipid loci (P range from 1.44 × 10 - 7 to 1.64 × 10 - 45). Of these, six coding variants showed population-specific associations and were independent of previously identified associations in European populations, including four low-frequency (PCSK9 p.Arg93Cys, HMGCR p.Tyr311Ser, APOA5 p.Gly185Cys and CETP p.Asp399Gly) and two common (APOB p.Arg532Trp and APOA4 p.Ser147Asn) variants. Furthermore, we detected three new lead non-coding variants at LPA, LIPC and LDLR in Chinese. The independent variants at PCSK9, HMGCR, LPA, APOA5 and LDLR were also associated with increased risk of coronary artery disease in the expected direction. In gene-based tests, the burden of rare or low frequency variants in PCSK9, HMGCR and CEPT exhibited strong associations with blood lipid levels (P < 2.8 × 10 - 6). Our findings identify additional population-specific possible causal variants. Our data demonstrate that the inter-ethnic differences in allele frequencies of coding variants may lead to different association signals across ethnic groups, highlighting the importance of including diverse populations to uncover genetic variation associated with lipid levels.
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Affiliation(s)
- Xiangfeng Lu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Jun Li
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China
| | - Huaixing Li
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Yang Chen
- Center for Genomic and Personalized Medicine, Medical Scientific Research Center and Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Laiyuan Wang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Meian He
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China
| | - Yiqin Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Liang Sun
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Yao Hu
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Jianfeng Huang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Feijie Wang
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Xuezhen Liu
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China
| | - Shufeng Chen
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Kuai Yu
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China
| | - Xueli Yang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, Medical Scientific Research Center and Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Xu Lin
- Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Tangchun Wu
- MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei, China
| | - Dongfeng Gu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
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