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Chen XY, Fang L, Zhang J, Zhong JM, Lin JJ, Lu F. The association of body mass index and its interaction with family history of dyslipidemia towards dyslipidemia in patients with type 2 diabetes: a cross-sectional study in Zhejiang Province, China. Front Public Health 2023; 11:1188212. [PMID: 37255759 PMCID: PMC10225544 DOI: 10.3389/fpubh.2023.1188212] [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: 03/20/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
Objectives This study aimed to investigate the association between body mass index (BMI) and dyslipidemia and to explore the interaction between BMI and family history of dyslipidemia towards dyslipidemia in patients with type 2 diabetes. Methods This cross-sectional study was conducted between March and November 2018 in Zhejiang Province, China. A total of 1,756 patients with type 2 diabetes were included, physical examination data, fasting blood samples and face-to-face questionnaire survey data were collected. Restricted cubic spline analysis was used to evaluate the association between BMI and the risk of dyslipidemia. Unconditional multivariable logistic regression was used to estimate the interaction between BMI and family history of dyslipidemia towards dyslipidemia. Results The prevalence of dyslipidemia was 53.7% in the study population. The risk of dyslipidemia elevated with increased BMI value (p for non-linearity <0.05). After adjusting for covariates, individuals with high BMI (≥24 kg/m2) and a family history of dyslipidemia had a 4.50-fold (95% CI: 2.99-6.78) increased risk of dyslipidemia compared to the normal reference group, which was higher than the risk associated with high BMI alone (OR = 1.83, 95% CI: 1.47-2.28) or family history of dyslipidemia alone (OR = 1.79 95% CI: 1.14-2.83). Significant additive interaction between high BMI and a family history of dyslipidemia was detected, with RERI, AP, and SI values of 1.88 (95% CI: 0.17-4.10), 0.42 (95% CI: 0.02-0.62), and 2.16 (95% CI: 1.07-4.37), respectively. However, stratified by status of diabetes control, this additive interaction was only find significant among patients with controlled diabetes. Conclusion Both high BMI and a family history of dyslipidemia were related with high risk of dyslipidemia. Moreover, there were synergistic interaction between these two factors. Patients with type 2 diabetes who had a family history of dyslipidemia were more susceptible to the negative impact of being overweight or obesity on dyslipidemia.
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Provido SMP, Abris GP, Lee H, Okekunle AP, Gironella GM, Capanzana MV, Chung GH, Hong S, Yu SH, Lee CB, Lee JE. Comparison of cardiovascular disease risk factors among FiLWHEL (2014-2016), NNS (2013) and KNHANES (2013-2015) women. BMC Womens Health 2023; 23:149. [PMID: 36997917 PMCID: PMC10064574 DOI: 10.1186/s12905-023-02218-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 02/08/2023] [Indexed: 04/01/2023] Open
Abstract
OBJECTIVES This study assessed the CVD risk factors among Filipino women (FW) in Korea and compared them with FW in the Philippines and women in Korea (KW). METHODS A cohort of 504 women from the Filipino Women's Health and Diet Study (FiLWHEL) aged 20-57 years old were age-matched (1:1 ratio) with women from the 2013 National Nutrition Survey in the Philippines and the 2013-2015 Korean National Health and Nutrition Examination Survey. Anthropometric data, blood pressure (BP), lipid and glucose levels were compared across the four populations by calculating the odds ratio (OR)s and 95% confidence interval (CI)s using conditional logistic regression models. RESULTS Compared to KW, FW in Korea and FW in the Philippines were more than 2 and 3 times higher odds of having obesity for BMI ≥ 30 kg/m2 and waist circumference ≥ 88 cm, respectively. However, FW in Korea had the highest odds (OR 5.51, 95% CI 3.18-9.56) of having hypertension compared to KW. FW in the Philippines had the highest odds of having dyslipidemia (compared to KW, total cholesterol ≥ 200 mg/dL: OR 8.83, 95% CI 5.30-14.71; LDL-C ≥ 130 mg/dL: OR 3.25, 95% CI 2.13-4.98; and triglyceride ≥ 150 mg/dL: OR 2.59, 95% CI 1.59-4.22), but FW in Korea and KW had similar prevalence of dyslipidemia. CONCLUSIONS FW in Korea had higher prevalence of obesity and hypertension, with similar prevalence of dyslipidemia compared to KW in this sample. FW in the Philippines had higher prevalence of dyslipidemia compared to FW in Korea. Further prospective studies are warranted to examine the CVD risk factors among continental and native-born Filipino women.
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Affiliation(s)
- Sherlyn Mae P Provido
- Research Institute of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, South Korea
| | - Grace P Abris
- School of Public Health, Loma Linda University, Loma Linda, California, USA
| | - Heejin Lee
- Department of Food and Nutrition, College of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Akinkunmi Paul Okekunle
- Research Institute of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, South Korea
| | - Glen Melvin Gironella
- Department of Science and Technology-Food and Nutrition Research Institute, DOST Compound, Gen. Santos Avenue, Bicutan, Taguig, Metro Manila, Philippines
| | - Mario V Capanzana
- Department of Science and Technology-Food and Nutrition Research Institute, DOST Compound, Gen. Santos Avenue, Bicutan, Taguig, Metro Manila, Philippines
| | - Grace H Chung
- Research Institute of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, South Korea
- Department of Child Development and Family Studies, College of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, South Korea
| | - Sangmo Hong
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, 153 Gyeongchun-ro, Guri, South Korea
| | - Sung Hoon Yu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, 153 Gyeongchun-ro, Guri, South Korea
| | - Chang Beom Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, 153 Gyeongchun-ro, Guri, South Korea
| | - Jung Eun Lee
- Research Institute of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, South Korea.
- Department of Food and Nutrition, College of Human Ecology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
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Polygenic Risk of Hypertriglyceridemia Is Modified by BMI. Int J Mol Sci 2022; 23:ijms23179837. [PMID: 36077235 PMCID: PMC9456481 DOI: 10.3390/ijms23179837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 12/03/2022] Open
Abstract
Background: Genetic risk scores (GRSs) have partially improved the understanding of the etiology of moderate hypertriglyceridemia (HTG), which until recently was mainly assessed by secondary predisposing causes. The main objective of this study was to assess whether this variability is due to the interaction between clinical variables and GRS. Methods: We analyzed 276 patients with suspected polygenic HTG. An unweighted GRS was developed with the following variants: c.724C > G (ZPR1 gene), c.56C > G (APOA5 gene), c.1337T > C (GCKR gene), g.19986711A > G (LPL gene), c.107 + 1647T > C (BAZ1B gene) and g.125478730A > T (TRIB gene). Interactions between the GRS and clinical variables (body mass index (BMI), diabetes mellitus, diet, physical activity, alcohol consumption, age and gender) were evaluated. Results: The GRS was associated with triglyceride (TG) concentrations. There was a significant interaction between BMI and GRS, with the intensity of the relationship between the number of alleles and the TG concentration being greater in individuals with a higher BMI. Conclusions: GRS is associated with plasma TG concentrations and is markedly influenced by BMI. This finding could improve the stratification of patients with a high genetic risk for HTG who could benefit from more intensive healthcare interventions.
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Roman YM, McClish D, Price ET, Sabo RT, Woodward OM, Mersha TB, Shah N, Armada A, Terkeltaub R. Cardiometabolic genomics and pharmacogenomics investigations in Filipino Americans: Steps towards precision health and reducing health disparities. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100136. [PMID: 35647570 PMCID: PMC9139029 DOI: 10.1016/j.ahjo.2022.100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 12/26/2022]
Abstract
Background Filipino Americans (FAs) are the third-largest Asian American subgroup in the United States (US). Some studies showed that FAs experience more cardiometabolic diseases (CMDs) than other Asian subgroups and non-Hispanic Whites. The increased prevalence of CMD observed in FAs could be due to genetics and social/dietary lifestyles. While FAs are ascribed as an Asian group, they have higher burdens of CMD, and adverse social determinants of health compared to other Asian subgroups. Therefore, studies to elucidate how FAs might develop CMD and respond to medications used to manage CMD are warranted. The ultimate goals of this study are to identify potential mechanisms for reducing CMD burden in FAs and to optimize therapeutic drug selection. Collectively, these investigations could reduce the cardiovascular health disparities among FAs. Rationale and design This is a cross-sectional epidemiological design to enroll 300 self-identified Filipino age 18 yrs. or older without a history of cancer and/or organ transplant from Virginia, Washington DC, and Maryland. Once consented, a health questionnaire and disease checklist are administered to participants, and anthropometric data and other vital signs are collected. When accessible, we collect blood samples to measure basic blood biochemistry, lipids, kidney, and liver functions. We also extract DNA from the blood or saliva for genetic and pharmacogenetic analyses. CMD prevalence in FAs will be compared to the US population. Finally, we will conduct multivariate analyses to ascertain the role of genetic and non-genetic factors in developing CMD in FAs. Virginia Commonwealth University IRB approved all study materials (Protocol HM20018500). Summary This is the first community-based study to involve FAs in genomics research. The study is actively recruiting participants. Participant enrollment is ongoing. At the time of this publication, the study has enrolled 97 participants. This ongoing study is expected to inform future research to reduce cardiovascular health disparities among FAs.
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Affiliation(s)
- Youssef M. Roman
- Department of Pharmacotherapy and Outcomes Science, 410 N 12th Street, Virginia Commonwealth University, School of Pharmacy, Richmond, VA 23298, United States of America
| | - Donna McClish
- Department of Biostatistics, 830 East Main Street, One Capitol Square 740, Virginia Commonwealth University, School of Medicine, Richmond, VA 23329, United States of America
| | - Elvin T. Price
- Department of Pharmacotherapy and Outcomes Science, 410 N 12th Street, Virginia Commonwealth University, School of Pharmacy, Richmond, VA 23298, United States of America
| | - Roy T. Sabo
- Department of Biostatistics, 830 East Main Street, One Capitol Square 740, Virginia Commonwealth University, School of Medicine, Richmond, VA 23329, United States of America
| | - Owen M. Woodward
- Department of Physiology, University of Maryland School of Medicine, 685 W. Baltimore St., HSF1 580F, Baltimore, MD 21201, United States of America
| | - Tesfaye B. Mersha
- Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati, 3333 Burnet Avenue, MLC 7037, Cincinnati, OH 45229-3026, United States of America
| | - Nehal Shah
- Division of Rheumatology, Allergy, and Immunology, 1112 East Clay Street, VCU Health Sciences Research Building, Room 4-110, Virginia Commonwealth University, School of Medicine, Richmond, VA 23298-0263, United States of America
| | - Andrew Armada
- Filipino American Association of Central Virginia, 7117 Galax Road, Richmond, VA 23228, United States of America
| | - Robert Terkeltaub
- 9-SDVAHCS, Division of Rheumatology, Allergy, and Immunology, USCD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, United States of America
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Wuni R, Kuhnle GGC, Wynn-Jones AA, Vimaleswaran KS. A Nutrigenetic Update on CETP Gene–Diet Interactions on Lipid-Related Outcomes. Curr Atheroscler Rep 2022; 24:119-132. [PMID: 35098451 PMCID: PMC8924099 DOI: 10.1007/s11883-022-00987-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2021] [Indexed: 02/08/2023]
Abstract
Purpose of Review An abnormal lipid profile is considered a main risk factor for cardiovascular diseases and evidence suggests that single nucleotide polymorphisms (SNPs) in the cholesteryl ester transfer protein (CETP) gene contribute to variations in lipid levels in response to dietary intake. The objective of this review was to identify and discuss nutrigenetic studies assessing the interactions between CETP SNPs and dietary factors on blood lipids. Recent Findings Relevant articles were obtained through a literature search of PubMed and Google Scholar through to July 2021. An article was included if it examined an interaction between CETP SNPs and dietary factors on blood lipids. From 49 eligible nutrigenetic studies, 27 studies reported significant interactions between 8 CETP SNPs and 17 dietary factors on blood lipids in 18 ethnicities. The discrepancies in the study findings could be attributed to genetic heterogeneity, and differences in sample size, study design, lifestyle and measurement of dietary intake. The most extensively studied ethnicities were those of Caucasian populations and majority of the studies reported an interaction with dietary fat intake. The rs708272 (TaqIB) was the most widely studied CETP SNP, where ‘B1’ allele was associated with higher CETP activity, resulting in lower high-density lipoprotein cholesterol and higher serum triglycerides under the influence of high dietary fat intake. Summary Overall, the findings suggest that CETP SNPs might alter blood lipid profiles by modifying responses to diet, but further large studies in multiple ethnic groups are warranted to identify individuals at risk of adverse lipid response to diet. Supplementary Information The online version contains supplementary material available at 10.1007/s11883-022-00987-y.
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Hasegawa T, Yamaguchi R, Kakuta M, Sawada K, Kawatani K, Murashita K, Nakaji S, Imoto S. Prediction of blood test values under different lifestyle scenarios using time-series electronic health record. PLoS One 2020; 15:e0230172. [PMID: 32196517 PMCID: PMC7083324 DOI: 10.1371/journal.pone.0230172] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 02/24/2020] [Indexed: 12/13/2022] Open
Abstract
Owing to increasing medical expenses, researchers have attempted to detect clinical signs and preventive measures of diseases using electronic health record (EHR). In particular, time-series EHRs collected by periodic medical check-up enable us to clarify the relevance among check-up results and individual environmental factors such as lifestyle. However, usually such time-series data have many missing observations and some results are strongly correlated to each other. These problems make the analysis difficult and there exists strong demand to detect clinical findings beyond them. We focus on blood test values in medical check-up results and apply a time-series analysis methodology using a state space model. It can infer the internal medical states emerged in blood test values and handle missing observations. The estimated models enable us to predict one's blood test values under specified condition and predict the effect of intervention, such as changes of body composition and lifestyle. We use time-series data of EHRs periodically collected in the Hirosaki cohort study in Japan and elucidate the effect of 17 environmental factors to 38 blood test values in elderly people. Using the estimated model, we then simulate and compare time-transitions of participant's blood test values under several lifestyle scenarios. It visualizes the impact of lifestyle changes for the prevention of diseases. Finally, we exemplify that prediction errors under participant's actual lifestyle can be partially explained by genetic variations, and some of their effects have not been investigated by traditional association studies.
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Affiliation(s)
- Takanori Hasegawa
- Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Rui Yamaguchi
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Masanori Kakuta
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Kaori Sawada
- Department of Social Medicine, Graduate School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan
| | - Kenichi Kawatani
- COI Research Initiatives Organization, Hirosaki University, Hirosaki, Aomori, Japan
| | - Koichi Murashita
- COI Research Initiatives Organization, Hirosaki University, Hirosaki, Aomori, Japan
| | - Shigeyuki Nakaji
- Department of Social Medicine, Graduate School of Medicine, Hirosaki University, Hirosaki, Aomori, Japan
| | - Seiya Imoto
- Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
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Williams PT. Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations. Sci Rep 2020; 10:4486. [PMID: 32161301 PMCID: PMC7066156 DOI: 10.1038/s41598-020-60965-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 02/17/2020] [Indexed: 12/16/2022] Open
Abstract
"Quantile-dependent expressivity" is a dependence of genetic effects on whether the phenotype (e.g., triglycerides) is high or low relative to its distribution in the population. Quantile-specific offspring-parent regression slopes (βOP) were estimated by quantile regression for 6227 offspring-parent pairs. Quantile-specific heritability (h2), estimated by 2βOP/(1 + rspouse), decreased 0.0047 ± 0.0007 (P = 2.9 × 10-14) for each one-percent decrement in fasting triglyceride concentrations, i.e., h2 ± SE were: 0.428 ± 0.059, 0.230 ± 0.030, 0.111 ± 0.015, 0.050 ± 0.016, and 0.033 ± 0.010 at the 90th, 75th, 50th, 25th, and 10th percentiles of the triglyceride distribution, respectively. Consistent with quantile-dependent expressivity, 11 drug studies report smaller genotype differences at lower (post-treatment) than higher (pre-treatment) triglyceride concentrations. This meant genotype-specific triglyceride changes could not move in parallel when triglycerides were decreased pharmacologically, so that subtracting pre-treatment from post-treatment triglyceride levels necessarily created a greater triglyceride decrease for the genotype with a higher pre-treatment value (purported precision-medicine genetic markers). In addition, sixty-five purported gene-environment interactions were found to be potentially attributable to triglyceride's quantile-dependent expressivity, including gene-adiposity (APOA5, APOB, APOE, GCKR, IRS-1, LPL, MTHFR, PCSK9, PNPLA3, PPARγ2), gene-exercise (APOA1, APOA2, LPL), gene-diet (APOA5, APOE, INSIG2, LPL, MYB, NXPH1, PER2, TNFA), gene-alcohol (ALDH2, APOA5, APOC3, CETP, LPL), gene-smoking (APOC3, CYBA, LPL, USF1), gene-pregnancy (LPL), and gene-insulin resistance interactions (APOE, LPL).
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Affiliation(s)
- Paul T Williams
- Lawrence Berkeley National Laboratory, Molecular Biophysics & Integrated Bioimaging Division 1 Cyclotron Road, Berkeley, CA, 94720, USA.
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Strawbridge RJ, Ward J, Bailey MES, Cullen B, Ferguson A, Graham N, Johnston KJA, Lyall LM, Pearsall R, Pell J, Shaw RJ, Tank R, Lyall DM, Smith DJ. Carotid Intima-Media Thickness: Novel Loci, Sex-Specific Effects, and Genetic Correlations With Obesity and Glucometabolic Traits in UK Biobank. Arterioscler Thromb Vasc Biol 2019; 40:446-461. [PMID: 31801372 PMCID: PMC6975521 DOI: 10.1161/atvbaha.119.313226] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Supplemental Digital Content is available in the text. Objective: Atherosclerosis is the underlying cause of most cardiovascular disease, but mechanisms underlying atherosclerosis are incompletely understood. Ultrasound measurement of the carotid intima-media thickness (cIMT) can be used to measure vascular remodeling, which is indicative of atherosclerosis. Genome-wide association studies have identified many genetic loci associated with cIMT, but heterogeneity of measurements collected by many small cohorts have been a major limitation in these efforts. Here, we conducted genome-wide association analyses in UKB (UK Biobank; N=22 179), the largest single study with consistent cIMT measurements. Approach and Results: We used BOLT-LMM software to run linear regression of cIMT in UKB, adjusted for age, sex, and genotyping chip. In white British participants, we identified 5 novel loci associated with cIMT and replicated most previously reported loci. In the first sex-specific analyses of cIMT, we identified a locus on chromosome 5, associated with cIMT in women only and highlight VCAN as a good candidate gene at this locus. Genetic correlations with body mass index and glucometabolic traits were also observed. Two loci influenced risk of ischemic heart disease. ConclusionS: These findings replicate previously reported associations, highlight novel biology, and provide new directions for investigating the sex differences observed in cardiovascular disease presentation and progression.
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Affiliation(s)
- Rona J Strawbridge
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Joey Ward
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Mark E S Bailey
- School of Life Sciences, College of Medical, Veterinary and Life Sciences (M.E.S.B., K.J.A.J.), University of Glasgow, United Kingdom
| | - Breda Cullen
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Amy Ferguson
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Nicholas Graham
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Keira J A Johnston
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom.,School of Life Sciences, College of Medical, Veterinary and Life Sciences (M.E.S.B., K.J.A.J.), University of Glasgow, United Kingdom.,Division of Psychiatry, College of Medicine, University of Edinburgh, United Kingdom (K.J.A.J.)
| | - Laura M Lyall
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Robert Pearsall
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Jill Pell
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Richard J Shaw
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom.,Health Data Research United Kingdom (R.J.S.).,Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden (R.J.S.)
| | - Rachana Tank
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Donald M Lyall
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
| | - Daniel J Smith
- From the Institute of Health and Wellbeing (R.J.S., J.W., B.C., A.F., N.G., K.J.A.J., L.M.L., R.P., J.P., R.J.S., R.T., D.M.L., D.J.S.), University of Glasgow, United Kingdom
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ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response. Nat Commun 2019; 10:3195. [PMID: 31324766 PMCID: PMC6642147 DOI: 10.1038/s41467-019-10967-7] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 06/11/2019] [Indexed: 12/13/2022] Open
Abstract
Genome analysis of diverse human populations has contributed to the identification of novel genomic loci for diseases of major clinical and public health impact. Here, we report a genome-wide analysis of type 2 diabetes (T2D) in sub-Saharan Africans, an understudied ancestral group. We analyze ~18 million autosomal SNPs in 5,231 individuals from Nigeria, Ghana and Kenya. We identify a previously-unreported genome-wide significant locus: ZRANB3 (Zinc Finger RANBP2-Type Containing 3, lead SNP p = 2.831 × 10−9). Knockdown or genomic knockout of the zebrafish ortholog results in reduction in pancreatic β-cell number which we demonstrate to be due to increased apoptosis in islets. siRNA transfection of murine Zranb3 in MIN6 β-cells results in impaired insulin secretion in response to high glucose, implicating Zranb3 in β-cell functional response to high glucose conditions. We also show transferability in our study of 32 established T2D loci. Our findings advance understanding of the genetics of T2D in non-European ancestry populations. Type 2 diabetes (T2D) is prevalent in populations worldwide, however, mostly studied in European and mixed-ancestry populations. Here, the authors perform a genome-wide association study for T2D in over 5,000 sub-Saharan Africans and identify a locus, ZRANB3, that is specific for this population.
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Hosseinzadeh N, Mehrabi Y, Daneshpour MS, Zayeri F, Guity K, Azizi F. Identifying new associated pleiotropic SNPs with lipids by simultaneous test of multiple longitudinal traits: An Iranian family-based study. Gene 2019; 692:156-169. [DOI: 10.1016/j.gene.2019.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 01/05/2019] [Accepted: 01/11/2019] [Indexed: 02/08/2023]
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Zhao X, Geng X, Srinivasasainagendra V, Chaudhary N, Judd S, Wadley V, Gutiérrez OM, Wang H, Lange EM, Lange LA, Woo D, Unverzagt FW, Safford M, Cushman M, Limdi N, Quarells R, Arnett DK, Irvin MR, Zhi D. A PheWAS study of a large observational epidemiological cohort of African Americans from the REGARDS study. BMC Med Genomics 2019; 12:26. [PMID: 30704471 PMCID: PMC6357353 DOI: 10.1186/s12920-018-0462-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Cardiovascular disease, diabetes, and kidney disease are among the leading causes of death and disability worldwide. However, knowledge of genetic determinants of those diseases in African Americans remains limited. RESULTS In our study, associations between 4956 GWAS catalog reported SNPs and 67 traits were examined among 7726 African Americans from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which is focused on identifying factors that increase stroke risk. The prevalent and incident phenotypes studied included inflammation, kidney traits, cardiovascular traits and cognition. Our results validated 29 known associations, of which eight associations were reported for the first time in African Americans. CONCLUSION Our cross-racial validation of GWAS findings provide additional evidence for the important roles of these loci in the disease process and may help identify genes especially important for future functional validation.
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Affiliation(s)
- Xueyan Zhao
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Xin Geng
- BGI-Shenzhen, Shenzhen, 518083 China
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | | | - Ninad Chaudhary
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Suzanne Judd
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Virginia Wadley
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Orlando M. Gutiérrez
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Henry Wang
- Department of Emergency Medicine, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 USA
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 USA
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH 45267 USA
| | - Frederick W. Unverzagt
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Monika Safford
- Division of General Internal Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065 USA
| | - Mary Cushman
- Department of Medicine and Pathology, Larner College of Medicine at the University of Vermont, Burlington, VT 05405 USA
| | - Nita Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Rakale Quarells
- Cardiovascular Research Institute, Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA 30310 USA
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY 40506 USA
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Degui Zhi
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
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12
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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: 1.2] [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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13
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Hebbar P, Nizam R, Melhem M, Alkayal F, Elkum N, John SE, Tuomilehto J, Alsmadi O, Thanaraj TA. Genome-wide association study identifies novel recessive genetic variants for high TGs in an Arab population. J Lipid Res 2018; 59:1951-1966. [PMID: 30108155 DOI: 10.1194/jlr.p080218] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 07/23/2018] [Indexed: 12/12/2022] Open
Abstract
Abnormal blood lipid levels are influenced by genetic and lifestyle/dietary factors. Although many genetic variants associated with blood lipid traits have been identified in Europeans, similar data in Middle Eastern populations are limited. We performed a genome-wide association study with Arab individuals (discovery cohort: 1,353; replication cohort: 1,176) from Kuwait to identify possible associations of genetic variants with high lipid levels. We used Illumina HumanOmniExpress BeadChip and candidate SNP genotyping in the discovery and replication phases, respectively. For association tests, we used genetic models that were based on additive and recessive modes of inheritance. High triglycerides (TGs) were recessively associated with six risk variants (rs1002487/RPS6KA1, rs11805972/LAD1) rs7761746/Or5v1, rs39745/CTTNBP2-LSM8, rs2934952/PGAP3, and rs9626773/RP11-191L9.4-CERK) at genome-wide significance (P 6.12E-09), and another six variants (rs10873925/ST6GALNAC5, rs4663379/SPP2-ARL4C, rs10033119/NPY1R, rs17709449/LINC00911-FLRT2, rs11654954/CDK12-NEUROD2, and rs9972882/STARD3) were associated at borderline significance (P 5.0E-08). High TG was also additively associated with rs11654954. All of the 12 identified markers are novel and are harbored in runs of homozygosity. Literature evidence supports the involvement of these gene loci in lipid-related processes. This study in an Arab population augments international efforts to identify genetic regulation of lipid traits.
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Affiliation(s)
- Prashantha Hebbar
- Dasman Diabetes Institute, Dasman 15462, Kuwait.,Faculty of Medicine, Univerisity of Helsinki, Helsinki, Finland
| | | | | | | | - Naser Elkum
- Dasman Diabetes Institute, Dasman 15462, Kuwait
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14
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Verma A, Lucas A, Verma SS, Zhang Y, Josyula N, Khan A, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger. Am J Hum Genet 2018; 102:592-608. [PMID: 29606303 PMCID: PMC5985339 DOI: 10.1016/j.ajhg.2018.02.017] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 02/20/2018] [Indexed: 01/23/2023] Open
Abstract
Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with ∼630,000 common frequency SNPs with minor allele frequency > 0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.
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Affiliation(s)
- Anurag Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Lucas
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shefali S Verma
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA
| | - Yu Zhang
- Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA
| | - Navya Josyula
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Anqa Khan
- Mount Holyoke College, South Hadley, MA 01075, USA
| | - Dustin N Hartzel
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Daniel R Lavage
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Joseph Leader
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA; Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA 17822, USA.
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15
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Abstract
PURPOSE OF REVIEW Rare large-effect genetic variants underlie monogenic dyslipidemias, whereas common small-effect genetic variants - single nucleotide polymorphisms (SNPs) - have modest influences on lipid traits. Over the past decade, these small-effect SNPs have been shown to cumulatively exert consistent effects on lipid phenotypes under a polygenic framework, which is the focus of this review. RECENT FINDINGS Several groups have reported polygenic risk scores assembled from lipid-associated SNPs, and have applied them to their respective phenotypes. For lipid traits in the normal population distribution, polygenic effects quantified by a score that integrates several common polymorphisms account for about 20-30% of genetic variation. Among individuals at the extremes of the distribution, that is, those with clinical dyslipidemia, the polygenic component includes both rare variants with large effects and common polymorphisms: depending on the trait, 20-50% of susceptibility can be accounted for by this assortment of genetic variants. SUMMARY Accounting for polygenic effects increases the numbers of dyslipidemic individuals who can be explained genetically, but a substantial proportion of susceptibility remains unexplained. Whether documenting the polygenic basis of dyslipidemia will affect outcomes in clinical trials or prospective observational studies remains to be determined.
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16
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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: 9] [Impact Index Per Article: 1.5] [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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17
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Spracklen CN, Chen P, Kim YJ, Wang X, Cai H, Li S, Long J, Wu Y, Wang YX, Takeuchi F, Wu JY, Jung KJ, Hu C, Akiyama K, Zhang Y, Moon S, Johnson TA, Li H, Dorajoo R, He M, Cannon ME, Roman TS, Salfati E, Lin KH, Guo X, Sheu WHH, Absher D, Adair LS, Assimes TL, Aung T, Cai Q, Chang LC, Chen CH, Chien LH, Chuang LM, Chuang SC, Du S, Fan Q, Fann CSJ, Feranil AB, Friedlander Y, Gordon-Larsen P, Gu D, Gui L, Guo Z, Heng CK, Hixson J, Hou X, Hsiung CA, Hu Y, Hwang MY, Hwu CM, Isono M, Juang JMJ, Khor CC, Kim YK, Koh WP, Kubo M, Lee IT, Lee SJ, Lee WJ, Liang KW, Lim B, Lim SH, Liu J, Nabika T, Pan WH, Peng H, Quertermous T, Sabanayagam C, Sandow K, Shi J, Sun L, Tan PC, Tan SP, Taylor KD, Teo YY, Toh SA, Tsunoda T, van Dam RM, Wang A, Wang F, Wang J, Wei WB, Xiang YB, Yao J, Yuan JM, Zhang R, Zhao W, Chen YDI, Rich SS, Rotter JI, Wang TD, Wu T, Lin X, Han BG, Tanaka T, Cho YS, Katsuya T, Jia W, Jee SH, Chen YT, Kato N, Jonas JB, Cheng CY, Shu XO, He J, Zheng W, Wong TY, Huang W, Kim BJ, Tai ES, Mohlke KL, Sim X. Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum Mol Genet 2017; 26:1770-1784. [PMID: 28334899 DOI: 10.1093/hmg/ddx062] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 02/16/2017] [Indexed: 12/28/2022] Open
Abstract
Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets.
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Affiliation(s)
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun, China
| | - Young Jin Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Xu Wang
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shengxu Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | | | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.,School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Keum-Ji Jung
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Cheng Hu
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Koichi Akiyama
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Yonghong Zhang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Sanghoon Moon
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Todd A Johnson
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Huaixing Li
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Maren E Cannon
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Tamara S Roman
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Elias Salfati
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Keng-Hung Lin
- Department of Ophthalmology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wayne H H Sheu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, National Defense Medical Center, Taipei, Taiwan.,Institute of Medical Technology, National Chung-Hsing University, Taichung, Taiwan
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.,School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Li-Hsin Chien
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Lee-Ming Chuang
- Division of Endocrinology & Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.,College of Medicine, National Taiwan University, Taipei, Taiwan.,Institute of Preventive Medicine, School of Public Health, National Taiwan University, Taipei, Taiwan
| | - Shu-Chun Chuang
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.,Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Qiao Fan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Cathy S J Fann
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Alan B Feranil
- USC-Office of Population Studies Foundation, Inc, University of San Carlos, Cebu City, Philippines.,Department of Anthropology, Sociology, and History, University of San Carlos, Cebu City, Philippines
| | - Yechiel Friedlander
- Unit of Epidemiology, Hebrew University-Hadassah Braun School of Public Health, Jerusalem, Israel
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.,Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Dongfeng Gu
- Department of Epidemiology and Population Genetics, Fuwai Hospital, Beijing, China
| | - Lixuan Gui
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhirong Guo
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - James Hixson
- Human Genetics Center, University of Texas School of Public Health, Houston, TX, USA
| | - Xuhong Hou
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Yao Hu
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Mi Yeong Hwang
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Chii-Min Hwu
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Masato Isono
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Jyh-Ming Jimmy Juang
- College of Medicine, National Taiwan University, Taipei, Taiwan.,Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,Department of Biochemistry, National University of Singapore, Singapore, Singapore
| | - Yun Kyoung Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan.,School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Sun-Ju Lee
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Social Work, Tunghai University, Taichung, Taiwan
| | - Kae-Woei Liang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan.,Department of Medicine, China Medical University, Taichung, Taiwan
| | - Blanche Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Sing-Hui Lim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Wen-Harn Pan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Hao Peng
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Kevin Sandow
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jinxiu Shi
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute, Shanghai, China
| | - Liang Sun
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Pok Chien Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Shu-Pei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Kent D Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yik-Ying Teo
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Life Sciences Institute, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore.,NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore.,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Sue-Anne Toh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore
| | - Aili Wang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Feijie Wang
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Jie Wang
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Science Key Lab, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Capital Medical University, Beijing, China
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.,Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Rong Zhang
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Wanting Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tzung-Dau Wang
- College of Medicine, National Taiwan University, Taipei, Taiwan.,Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Tangchun Wu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xu Lin
- Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of the Chinese Academy of Sciences, Shanghai, China
| | - Bok-Ghee Han
- Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - Toshihiro Tanaka
- Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
| | - Weiping Jia
- Shanghai Diabetes Institute, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Sun-Ha Jee
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, South Korea
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.,Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University of Heidelberg, Mannheim, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center and Shanghai Industrial Technology Institute, Shanghai, China
| | - Bong-Jo Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, South Korea
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School Singapore, Singapore, Singapore.,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore
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18
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Feng Q, Wei WQ, Levinson RT, Mosley JD, Stein CM. Replication and fine-mapping of genetic predictors of lipid traits in African-Americans. J Hum Genet 2017; 62:895-901. [PMID: 28539666 PMCID: PMC5612856 DOI: 10.1038/jhg.2017.55] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 04/17/2017] [Accepted: 04/20/2017] [Indexed: 12/16/2022]
Abstract
Circulating lipid concentrations are among the strongest modifiable risk factors for coronary artery disease (CAD). Most genetic studies have focused on Caucasian populations with little information available for populations of African ancestry. Using a cohort of ~2800 African-Americans (AAs) from a biobank at Vanderbilt University (BioVU), we sought to trans-ethnically replicate genetic variants reported by the Global Lipids Genetics Consortium to be associated with lipid traits in Caucasians, followed by fine-mapping those loci using all available variants on the MetaboChip. In AAs, we replicated one of 56 SNPs for total cholesterol (TC) (rs6511720 in LDLR, P=2.15 × 10-8), one of 63 SNPs for high-density lipoprotein cholesterol (HDL-C) (rs3764261 in CETP, P=1.13 × 10-5), two of 46 SNPs for low-density lipoprotein cholesterol (LDL-C) (rs629301 in CELSR2/SORT1, P=1.11 × 10-5 and rs6511720 in LDLR, P=2.47 × 10-5) and one of 34 SNPs for TG (rs645040 in MSL2L1, P=4.29 × 10-4). Using all available variants on MetaboChip for fine mapping, we identified additional variants associated with TC (APOE), HDL-C (LPL and CETP) and LDL-C (APOE). Furthermore, we identified two loci significantly associated with non-HDL-C: APOE/APOC1/TOMM40 and PCSK9. In conclusion, the genetic architecture of lipid traits in AAs differs substantially from that in Caucasians and it remains poorly characterized.
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Affiliation(s)
- QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rebecca T Levinson
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan D Mosley
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - C Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
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19
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Morrison AC, Huang Z, Yu B, Metcalf G, Liu X, Ballantyne C, Coresh J, Yu F, Muzny D, Feofanova E, Rustagi N, Gibbs R, Boerwinkle E. Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits. Am J Hum Genet 2017; 100:205-215. [PMID: 28089252 DOI: 10.1016/j.ajhg.2016.12.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 12/14/2016] [Indexed: 01/11/2023] Open
Abstract
Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.
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Affiliation(s)
- Alanna C Morrison
- Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030, USA.
| | - Zhuoyi Huang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bing Yu
- Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xiaoming Liu
- Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030, USA
| | - Christie Ballantyne
- Section of Cardiovascular Research, Baylor College of Medicine, Houston, TX 77030, USA; Houston Methodist Debakey Heart and Vascular Center, Houston, TX 77030, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21287, USA
| | - Fuli Yu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Elena Feofanova
- Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030, USA
| | - Navin Rustagi
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Richard Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas School of Public Health, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
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20
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Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits. Nat Genet 2016; 48:867-76. [PMID: 27399967 PMCID: PMC5145000 DOI: 10.1038/ng.3607] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 06/03/2016] [Indexed: 12/15/2022]
Abstract
Hematologic measures such as hematocrit and white blood cell (WBC) count are heritable and clinically relevant. Erythrocyte and WBC phenotypes were analyzed with Illumina HumanExome BeadChip genotypes in 52,531 individuals (37,775 of European ancestry; 11,589 African Americans; 3,167 Hispanic Americans) from 16 population-based cohorts. We then performed replication analyses of novel discoveries in 18,018 European American women and 5,261 Han Chinese. We identified and replicated four novel erythrocyte trait-locus associations (CEP89, SHROOM3, FADS2, and APOE) and six novel WBC loci for neutrophil count (S1PR4), monocyte count (BTBD8, NLRP12, and IL17RA), eosinophil count (IRF1), and total WBC (MYB). The novel association of a rare missense variant in S1PR4 supports the role of sphingosine-1-phosphate signaling in leukocyte trafficking and circulating neutrophil counts. Loss-of-function experiments of S1pr4 in mouse and zebrafish demonstrated phenotypes consistent with the association observed in humans and altered kinetics of neutrophil recruitment and resolution in response to tissue injury.
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21
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Klimentidis YC, Arora A. Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants. ACTA ACUST UNITED AC 2016; 9:154-61. [PMID: 26850992 DOI: 10.1161/circgenetics.115.001246] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 01/27/2016] [Indexed: 12/24/2022]
Abstract
BACKGROUND Several studies suggest that some triglyceride-associated single-nucleotide polymorphisms (SNPs) have pleiotropic and opposite effects on glycemic traits. This potentially implicates them in pathways such as de novo lipogenesis, which is presumably upregulated in the context of insulin resistance. We therefore tested whether the association of triglyceride-associated SNPs with triglyceride levels differs according to one's level of insulin resistance. METHODS AND RESULTS In 3 cohort studies (combined n=12 487), we tested the interaction of established triglyceride-associated SNPs (individually and collectively) with several traits related to insulin resistance, on triglyceride levels. We also tested the interaction of triglyceride SNPs with fasting insulin-associated SNPs, individually and collectively, on triglyceride levels. We find significant interactions of a weighted genetic risk score for triglycerides with insulin resistance on triglyceride levels (Pinteraction=2.73×10(-11) and Pinteraction=2.48×10(-11) for fasting insulin and homeostasis model assessment of insulin resistance, respectively). The association of the triglyceride genetic risk score with triglyceride levels is >60% stronger among those in the highest tertile of homeostasis model assessment of insulin resistance compared with those in the lowest tertile. Individual SNPs contributing to this trend include those in/near GCKR, CILP2, and IRS1, whereas PIGV-NROB2 and LRPAP1 display an opposite trend of interaction. In the pooled data set, we also identify a SNP-by-SNP interaction involving a triglyceride-associated SNP, rs4722551 near MIR148A, with a fasting insulin-associated SNP, rs4865796 in ARL15 (Pinteraction=4.1×10(-5)). CONCLUSIONS Our findings may thus provide genetic evidence for the upregulation of triglyceride levels in insulin-resistant individuals, in addition to identifying specific genetic loci and a SNP-by-SNP interaction implicated in this process.
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Affiliation(s)
- Yann C Klimentidis
- From the Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson.
| | - Amit Arora
- From the Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson
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22
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Abstract
PURPOSE OF REVIEW Recent genome-wide association studies have identified numerous common genetic variants associated with plasma lipid traits and have provided new insights into the regulation of lipoprotein metabolism including the identification of novel biological processes. These findings add to a body of existing data on dietary and environmental factors affecting plasma lipids. Here we explore how interactions between genetic risk factors and other phenotypes may explain some of the missing heritability of plasma lipid traits. RECENT FINDINGS Recent studies have identified true statistical interaction between several environmental and genetic risk factors and their effects on plasma lipid fractions. These include interactions between behaviors such as smoking or exercise as well as specific dietary nutrients and the effect size of specific genetic variants on plasma lipid traits risk and modifying effects of measures of adiposity on the cumulative impact of a number of common genetic variants on each of plasma triglycerides and HDL cholesterol. SUMMARY Interactions between genetic risk factors and clinical phenotypes may account for some of the unexplained heritability of plasma lipid traits. Recent studies provide biological insight into specific genetic associations and may aid in the identification of dyslipidemic patients for whom specific lifestyle interventions are likely to be most effective.
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Affiliation(s)
- Christopher B Cole
- aAtherogenomics Laboratory bRuddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
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23
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Cole CB, Nikpay M, Lau P, Stewart AFR, Davies RW, Wells GA, Dent R, McPherson R. Adiposity significantly modifies genetic risk for dyslipidemia. J Lipid Res 2014; 55:2416-22. [PMID: 25225679 PMCID: PMC4617143 DOI: 10.1194/jlr.p052522] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Recent genome-wide association studies have identified multiple loci robustly associated with plasma lipids, which also contribute to extreme lipid phenotypes. However, these common genetic variants explain <12% of variation in lipid traits. Adiposity is also an important determinant of plasma lipoproteins, particularly plasma TGs and HDL cholesterol (HDLc) concentrations. Thus, interactions between genes and clinical phenotypes may contribute to this unexplained heritability. We have applied a weighted genetic risk score (GRS) for both plasma TGs and HDLc in two large cohorts at the extremes of BMI. Both BMI and GRS were strongly associated with these lipid traits. A significant interaction between obese/lean status and GRS was noted for each of TG (PInteraction = 2.87 × 10−4) and HDLc (PInteraction = 1.05 × 10−3). These interactions were largely driven by SNPs tagging APOA5, glucokinase receptor (GCKR), and LPL for TG, and cholesteryl ester transfer protein (CETP), GalNAc-transferase (GALNT2), endothelial lipase (LIPG), and phospholipid transfer protein (PLTP) for HDLc. In contrast, the GRSLDL cholesterol × adiposity interaction was not significant. Sexual dimorphism was evident for the GRSHDL on HDLc in obese (PInteraction = 0.016) but not lean subjects. SNP by BMI interactions may provide biological insight into specific genetic associations and missing heritability.
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Affiliation(s)
- Christopher B Cole
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Majid Nikpay
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Paulina Lau
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Alexandre F R Stewart
- Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Robert W Davies
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - George A Wells
- Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Canada
| | - Robert Dent
- Bariatric Centre of Excellence, Ottawa Hospital, Ottawa, Canada
| | - Ruth McPherson
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Canada Ruddy Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Canada
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24
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Zubair N, Mayer-Davis EJ, Mendez MA, Mohlke KL, North KE, Adair LS. Genetic risk score and adiposity interact to influence triglyceride levels in a cohort of Filipino women. Nutr Diabetes 2014; 4:e118. [PMID: 24932782 PMCID: PMC4079926 DOI: 10.1038/nutd.2014.16] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Revised: 02/18/2014] [Accepted: 02/20/2014] [Indexed: 01/19/2023] Open
Abstract
Background/Objectives: Individually, genetic variants only moderately influence cardiometabolic (CM) traits, such as lipid and inflammatory markers. In this study we generated genetic risk scores from a combination of previously reported variants influencing CM traits, and used these scores to explore how adiposity levels could mediate genetic contributions to CM traits. Subjects/Methods: Participants included 1649 women from the 2005 Cebu Longitudinal Health and Nutrition Survey. Three genetic risk scores were constructed for C-reactive protein (CRP), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TGs). We used linear regression models to assess the association between each genetic risk score and its related trait. We also tested for interactions between each score and measures of adiposity. Results: Each genetic risk score explained a greater proportion of variance in trait levels than any individual genetic variant. We found an interaction between the TG genetic risk score (2.29–14.34 risk alleles) and waist circumference (WC) (Pinteraction=1.66 × 10−2). Based on model predictions, for individuals with a higher TG genetic risk score (75th percentile=12), having an elevated WC (⩾80 cm) increased TG levels from 1.32 to 1.71 mmol l−1. However, for individuals with a lower score (25th percentile=7), having an elevated WC did not significantly change TG levels. Conclusions: The TG genetic risk score interacted with adiposity to synergistically influence TG levels. For individuals with a genetic predisposition to elevated TG levels, our results suggest that reducing adiposity could possibly prevent further increases in TG levels and thereby lessen the likelihood of adverse health outcomes such as cardiovascular disease.
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Affiliation(s)
- N Zubair
- Public Health Sciences Division, Cancer Prevention, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - E J Mayer-Davis
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - M A Mendez
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K E North
- Department of Epidemiology and Carolina Center for Genome Sciences, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - L S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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