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Mauriello A, Ascrizzi A, Molinari R, Falco L, Caturano A, D’Andrea A, Russo V. Pharmacogenomics of Cardiovascular Drugs for Atherothrombotic, Thromboembolic and Atherosclerotic Risk. Genes (Basel) 2023; 14:2057. [PMID: 38003001 PMCID: PMC10671139 DOI: 10.3390/genes14112057] [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: 09/30/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE OF REVIEW Advances in pharmacogenomics have paved the way for personalized medicine. Cardiovascular diseases still represent the leading cause of mortality in the world. The aim of this review is to summarize the background, rationale, and evidence of pharmacogenomics in cardiovascular medicine, in particular, the use of antiplatelet drugs, anticoagulants, and drugs used for the treatment of dyslipidemia. RECENT FINDINGS Randomized clinical trials have supported the role of a genotype-guided approach for antiplatelet therapy in patients with coronary heart disease undergoing percutaneous coronary interventions. Numerous studies demonstrate how the risk of ineffectiveness of new oral anticoagulants and vitamin K anticoagulants is linked to various genetic polymorphisms. Furthermore, there is growing evidence to support the association of some genetic variants and poor adherence to statin therapy, for example, due to the appearance of muscular symptoms. There is evidence for resistance to some drugs for the treatment of dyslipidemia, such as anti-PCSK9. SUMMARY Pharmacogenomics has the potential to improve patient care by providing the right drug to the right patient and could guide the identification of new drug therapies for cardiovascular disease. This is very important in cardiovascular diseases, which have high morbidity and mortality. The improvement in therapy could be reflected in the reduction of healthcare costs and patient mortality.
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Affiliation(s)
- Alfredo Mauriello
- Cardiology Unit, Department of Medical Translational Science, University of Campania “Luigi Campania”—Monaldi Hospital, 80126 Naples, Italy; (A.M.); (A.A.); (R.M.); (L.F.); (A.D.)
| | - Antonia Ascrizzi
- Cardiology Unit, Department of Medical Translational Science, University of Campania “Luigi Campania”—Monaldi Hospital, 80126 Naples, Italy; (A.M.); (A.A.); (R.M.); (L.F.); (A.D.)
| | - Riccardo Molinari
- Cardiology Unit, Department of Medical Translational Science, University of Campania “Luigi Campania”—Monaldi Hospital, 80126 Naples, Italy; (A.M.); (A.A.); (R.M.); (L.F.); (A.D.)
| | - Luigi Falco
- Cardiology Unit, Department of Medical Translational Science, University of Campania “Luigi Campania”—Monaldi Hospital, 80126 Naples, Italy; (A.M.); (A.A.); (R.M.); (L.F.); (A.D.)
| | - Alfredo Caturano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, 80100 Naples, Italy;
| | - Antonello D’Andrea
- Cardiology Unit, Department of Medical Translational Science, University of Campania “Luigi Campania”—Monaldi Hospital, 80126 Naples, Italy; (A.M.); (A.A.); (R.M.); (L.F.); (A.D.)
- Unit of Cardiology, “Umberto I” Hospital, Nocera Inferiore, 84014 Salerno, Italy
| | - Vincenzo Russo
- Cardiology Unit, Department of Medical Translational Science, University of Campania “Luigi Campania”—Monaldi Hospital, 80126 Naples, Italy; (A.M.); (A.A.); (R.M.); (L.F.); (A.D.)
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Lee EY, Akhtari F, House JS, Simpson RJ, Schmitt CP, Fargo DC, Schurman SH, Hall JE, Motsinger-Reif AA. Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study. ENVIRONMENTAL RESEARCH 2022; 212:113463. [PMID: 35605674 DOI: 10.1016/j.envres.2022.113463] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/01/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.
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Affiliation(s)
- Eunice Y Lee
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida Akhtari
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA; Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Ross J Simpson
- Department of Epidemiology, Gillings School of Public Health and Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Charles P Schmitt
- National Toxicology Program, National Institute of Health, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shepherd H Schurman
- Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Janet E Hall
- Clinical Research Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Alison A Motsinger-Reif
- Biostatistics & Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA.
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Identifying the Potential Roles of PBX4 in Human Cancers Based on Integrative Analysis. Biomolecules 2022; 12:biom12060822. [PMID: 35740947 PMCID: PMC9221482 DOI: 10.3390/biom12060822] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 02/05/2023] Open
Abstract
PBX4 belongs to the pre-B-cell leukemia homeobox (PBX) transcription factors family and acts as a transcriptional cofactor of HOX proteins participating in several pathophysiological processes. Recent studies have revealed that the dysregulation of PBX4 is closely related to multiple diseases, especially cancers. However, the research on PBX4’s potential roles in 33 cancers from the Cancer Genome Atlas (TCGA) is still insufficient. Therefore, we performed a comprehensive pan-cancer analysis to explore the roles of PBX4with multiple public databases. Our results showed that PBX4 was differentially expressed in 17 types of human cancer and significantly correlated to the pathological stage, tumor grade, and immune and molecular subtypes. We used the Kaplan–Meier plotter and PrognoScan databases to find the significant associations between PBX4 expression and prognostic values of multiple cancers. It was also found that PBX4 expression was statistically related to mutation status, DNA methylation, immune infiltration, drug sensitivity, and immune checkpoint blockade (ICB) therapy. Additionally, we found that PBX4 was involved in different functional states of multiple cancers from the single-cell resolution perspective. Enrichment analysis results showed that PBX4-related genes were enriched in the cell cycle process, MAPK cascade, ncRNA metabolic process, positive regulation of GTPase activity, and regulation of lipase activity and mainly participated in the pathways of cholesterol metabolism, base excision repair, herpes simplex virus 1 infection, transcriptional misregulation in cancer, and Epstein–Barr virus infection. Altogether, our integrative analysis could help in better understanding the potential roles of PBX4 in different human cancers.
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House JS, Motsinger-Reif AA. Fibrate pharmacogenomics: expanding past the genome. Pharmacogenomics 2020; 21:293-306. [PMID: 32180510 DOI: 10.2217/pgs-2019-0140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Fibrates are a medication class prescribed for decades as 'broad-spectrum' lipid-modifying agents used to lower blood triglyceride levels and raise high-density lipoprotein cholesterol levels. Such lipid changes are associated with a decrease in cardiovascular disease, and fibrates are commonly used to reduce risk of dangerous cardiovascular outcomes. As with most drugs, it is well established that response to fibrate treatment is variable, and this variation is heritable. This has motivated the investigation of pharmacogenomic determinants of response, and multiple studies have discovered a number of genes associated with fibrate response. Similar to other complex traits, the interrogation of single nucleotide polymorphisms using candidate gene or genome-wide approaches has not revealed a substantial portion of response variation. However, recent innovations in technological platforms and advances in statistical methodologies are revolutionizing the use and integration of other 'omes' in pharmacogenomics studies. Here, we detail successes, challenges, and recent advances in fibrate pharmacogenomics.
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Affiliation(s)
- John S House
- Division of Intramural Research, National Institute of Environmental Health Sciences, NIH, Department of Health & Human Services, Research Triangle Park, NC 27709, USA
| | - Alison A Motsinger-Reif
- Division of Intramural Research, National Institute of Environmental Health Sciences, NIH, Department of Health & Human Services, Research Triangle Park, NC 27709, 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: 1.2] [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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Woo Y, Shin JS, Shim CY, Kim JS, Kim BK, Park S, Chang HJ, Hong GR, Ko YG, Kang SM, Choi D, Ha JW, Hong MK, Jang Y, Lee SH. Effect of fenofibrate in 1113 patients at low-density lipoprotein cholesterol goal but high triglyceride levels: Real-world results and factors associated with triglyceride reduction. PLoS One 2018; 13:e0205006. [PMID: 30286170 PMCID: PMC6171908 DOI: 10.1371/journal.pone.0205006] [Citation(s) in RCA: 6] [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: 05/13/2018] [Accepted: 09/18/2018] [Indexed: 01/01/2023] Open
Abstract
Fibrates are used in patients with dyslipidemia and high cardiovascular risk. However, information regarding drug response to fibrate has been highly limited. We investigated treatment results and factors associated with triglyceride reduction after fenofibrate therapy using large-scale real-world data. Patients with one or more cardiovascular risk factors, at low-density lipoprotein-cholesterol goal but with triglyceride level ≥150 mg/dL, and undergoing treatment with fenofibrate 135–160 mg for the first time were included in this retrospective observational study. The outcome variable was the percentage changes of TG levels. The achievement rate of triglyceride <150 mg/dL was additionally analyzed. Factors associated with treatment results were also analyzed. Among 2546 patients who were initially screened, 1113 patients were enrolled (median age: 61 years; male: 71%). After median follow-up of 4 months, the median change in triglyceride was -60%, and 49% of the patients reached triglyceride <150 mg/dL. After adjusting for confounding variables, female sex, non-diabetic status, coronary artery disease, lower baseline triglyceride, and no statin use were identified to be independently associated with achievement of triglyceride <150 mg/dL. Among them, female sex, non-diabetic status, and coronary artery disease were also related to median or greater percentage reduction of triglyceride. In conclusion, only half of the study patients reached triglyceride levels <150 mg/dL after real-world fenofibrate therapy. This study indicates that more attention is needed on some subgroups to obtain optimal triglyceride levels when treating with fenofibrate.
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Affiliation(s)
- Yeongmin Woo
- Division of Cardiology, Department of Internal Medicine, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea
| | - Jeong-soo Shin
- Department of Biostatistics and Computing, Yonsei University College of Medicine, Seoul, Korea
| | - Chi-Young Shim
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Jung-Sun Kim
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Byeong-Keuk Kim
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Sungha Park
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Hyuk-Jae Chang
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Geu-Ru Hong
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Young-Guk Ko
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Min Kang
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Donghoon Choi
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Jong-Won Ha
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Myeong-Ki Hong
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Yangsoo Jang
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Sang-Hak Lee
- Division of Cardiology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea
- * E-mail:
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Cox JW, Patel D, Chung J, Zhu C, Lent S, Fisher V, Pitsillides A, Farrer L, Zhang X. An efficient analytic approach in genome-wide identification of methylation quantitative trait loci response to fenofibrate treatment. BMC Proc 2018; 12:44. [PMID: 30275893 PMCID: PMC6157188 DOI: 10.1186/s12919-018-0152-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Background The study of DNA methylation quantitative trait loci (meQTLs) helps dissect regulatory mechanisms underlying genetic associations of human diseases. In this study, we conducted the first genome-wide examination of genetic drivers of methylation variation in response to a triglyceride-lowering treatment with fenofibrate (response-meQTL) by using an efficient analytic approach. Methods Subjects (n = 429) from the GAW20 real data set with genotype and both pre- (visit 2) and post- (visit 4) fenofibrate treatment methylation measurements were included. Following the quality control steps of removing certain cytosine-phosphate-guanine (CpG) probes, the post−/premethylation changes (post/pre) were log transformed and the association was performed on 208,449 CpG sites. An additive linear mixed-effects model was used to test the association between each CpG probe and single nucleotide polymorphisms (SNPs) around ±1 Mb region, with age, sex, smoke, batch effect, and principal components included as covariates. Bonferroni correction was applied to define the significance threshold (p < 5.6 × 10− 10, given a total of 89,217,303 tests). Finally, we integrated our response-meQTL (re-meQTL) findings with the published genome-wide association study (GWAS) catalog of human diseases/traits. Results We identified 1087 SNPs as cis re-meQTLs associated with 610 CpG probes/sites located in 351 unique gene loci. Among these 1087 cis re-meQTL SNPs, 229 were unique and 6 were co-localized at 8 unique disease/trait loci reported in the GWAS catalog (enrichment p = 1.51 × 10− 23). Specifically, a lipid SNP, rs10903129, located in intron regions of gene TMEM57, was a re-meQTL (p = 3.12 × 10− 36) associated with the CpG probe cg09222892, which is in the upstream region of the gene RHCE, indicating a new target gene for rs10903129. In addition, we found that SNP rs12710728 has a suggestive association with cg17097782 (p = 1.77 × 10− 4), and that this SNP is in high linkage disequilibrium (LD) (R2 > 0.8) with rs7443270, which was previously reported to be associated with fenofibrate response (p = 5.00 × 10− 6). Conclusions By using a novel analytic approach, we efficiently identified thousands of cis re-meQTLs that provide a unique resource for further characterizing functional roles and gene targets of the SNPs that are most responsive to fenofibrate treatment. Our efficient analytic approach can be extended to large response quantitative trait locus studies with large sample sizes and multiple time points data.
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Affiliation(s)
- Jiayi Wu Cox
- 1Department of Genetics and Genomics, Boston University, 72 E Concord St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Devanshi Patel
- 2Department of Bioinformatics, Boston University, 72 E Concord St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Jaeyoon Chung
- 2Department of Bioinformatics, Boston University, 72 E Concord St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Congcong Zhu
- 4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Samantha Lent
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA
| | - Virginia Fisher
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA
| | - Achilleas Pitsillides
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA
| | - Lindsay Farrer
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
| | - Xiaoling Zhang
- 3Department of Biostatistics, Boston University School of Public Health, Boston University, 715 Albany St, Boston, MA 02118 USA.,4Section of Biomedical Genetics, Boston University, 72 E Concord St, Boston, MA 02118 USA
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Abstract
Blood lipids are important modifiable risk factors for coronary heart disease and various drugs have been developed to target lipid fractions. Considerable efforts have been made to identify genetic variants that modulate responses to drugs in the hope of optimizing their use. Pharmacogenomics and new biotechnologies now allow for meaningful integration of human genetic findings and therapeutic development for increased efficiency and precision of lipid-lowering drugs. Polygenic predictors of disease risk are also changing how patient populations can be stratified, enabling targeted therapeutic interventions to patients more likely to derive the highest benefit, marking a shift from single variant to genomic approaches in pharmacogenomics.
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Affiliation(s)
- Marc-André Legault
- Montreal Heart Institute, Montreal, QC, H1T 1C8, Canada.,Université de Montréal, Faculté de médecine, Montreal, QC, H3T 1J4, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, H1T 1C8, QC, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, QC, H1T 1C8, Canada.,Université de Montréal, Faculté de médecine, Montreal, QC, H3T 1J4, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, QC, H1T 1C8, Canada.,Université de Montréal, Faculté de médecine, Montreal, QC, H3T 1J4, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, H1T 1C8, QC, Canada
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9
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Geng X, Irvin MR, Hidalgo B, Aslibekyan S, Srinivasasainagendra V, An P, Frazier-Wood AC, Tiwari HK, Dave T, Ryan K, Ordovas JM, Straka RJ, Feitosa MF, Hopkins PN, Borecki I, Province MA, Mitchell BD, Arnett DK, Zhi D. An exome-wide sequencing study of lipid response to high-fat meal and fenofibrate in Caucasians from the GOLDN cohort. J Lipid Res 2018; 59:722-729. [PMID: 29463568 PMCID: PMC5880495 DOI: 10.1194/jlr.p080333] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 02/04/2018] [Indexed: 12/30/2022] Open
Abstract
Our understanding of genetic influences on the response of lipids to specific interventions is limited. In this study, we sought to elucidate effects of rare genetic variants on lipid response to a high-fat meal challenge and fenofibrate (FFB) therapy in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) cohort using an exome-wide sequencing-based association study. Our results showed that the rare coding variants in ITGA7, SIPA1L2, and CEP72 are significantly associated with fasting LDL cholesterol response to FFB (P = 1.24E-07), triglyceride postprandial area under the increase (AUI) (P = 2.31E-06), and triglyceride postprandial AUI response to FFB (P = 1.88E-06), respectively. We sought to replicate the association for SIPA1L2 in the Heredity and Phenotype Intervention (HAPI) Heart Study, which included a high-fat meal challenge but not FFB treatment. The associated rare variants in GOLDN were not observed in the HAPI Heart study, and thus the gene-based result was not replicated. For functional validation, we found that gene transcript level of SIPA1L2 is associated with triglyceride postprandial AUI (P < 0.05) in GOLDN. Our study suggests unique genetic mechanisms contributing to the lipid response to the high-fat meal challenge and FFB therapy.
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Affiliation(s)
- Xin Geng
- School of Biomedical Informatics The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Marguerite R Irvin
- Departments of Epidemiology University of Alabama at Birmingham, Birmingham, AL 35233
| | - Bertha Hidalgo
- Departments of Epidemiology University of Alabama at Birmingham, Birmingham, AL 35233
| | - Stella Aslibekyan
- Departments of Epidemiology University of Alabama at Birmingham, Birmingham, AL 35233
| | | | - Ping An
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110
| | - Alexis C Frazier-Wood
- US Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX 77030
| | - Hemant K Tiwari
- Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35233
| | - Tushar Dave
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Kathleen Ryan
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111; Instituto Madrileño de Estudios Avanzados en Alimentación, Madrid 28049, Spain; Centro Nacional Investigaciones Cardiovasculares, Madrid 28029, Spain
| | - Robert J Straka
- Department of Experimental and Clinical Pharmacology Minneapolis, University of Minnesota, MN 55455
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110
| | - Paul N Hopkins
- Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT 84112
| | - Ingrid Borecki
- Genetic Analysis Center, Department of Biostatistics, University of Washington, Seattle, WA 98105
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY 40506.
| | - Degui Zhi
- School of Biomedical Informatics The University of Texas Health Science Center at Houston, Houston, TX 77030; School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030.
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10
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Rotroff DM, Pijut SS, Marvel SW, Jack JR, Havener TM, Pujol A, Schluter A, Graf GA, Ginsberg HN, Shah HS, Gao H, Morieri ML, Doria A, Mychaleckyi JC, McLeod HL, Buse JB, Wagner MJ, Motsinger-Reif AA. Genetic Variants in HSD17B3, SMAD3, and IPO11 Impact Circulating Lipids in Response to Fenofibrate in Individuals With Type 2 Diabetes. Clin Pharmacol Ther 2018; 103:712-721. [PMID: 28736931 PMCID: PMC5828950 DOI: 10.1002/cpt.798] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 06/15/2017] [Accepted: 07/11/2017] [Indexed: 12/27/2022]
Abstract
Individuals with type 2 diabetes (T2D) and dyslipidemia are at an increased risk of cardiovascular disease. Fibrates are a class of drugs prescribed to treat dyslipidemia, but variation in response has been observed. To evaluate common and rare genetic variants that impact lipid responses to fenofibrate in statin-treated patients with T2D, we examined lipid changes in response to fenofibrate therapy using a genomewide association study (GWAS). Associations were followed-up using gene expression studies in mice. Common variants in SMAD3 and IPO11 were marginally associated with lipid changes in black subjects (P < 5 × 10-6 ). Rare variant and gene expression changes were assessed using a false discovery rate approach. AKR7A3 and HSD17B13 were associated with lipid changes in white subjects (q < 0.2). Mice fed fenofibrate displayed reductions in Hsd17b13 gene expression (q < 0.1). Associations of variants in SMAD3, IPO11, and HSD17B13, with gene expression changes in mice indicate that transforming growth factor-beta (TGF-β) and NRF2 signaling pathways may influence fenofibrate effects on dyslipidemia in patients with T2D.
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Affiliation(s)
- Daniel M Rotroff
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Sonja S Pijut
- Department of Pharmaceutical Sciences, University of Kentucky, Lexington, Kentucky, USA
| | - Skylar W Marvel
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - John R Jack
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Tammy M Havener
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA
| | - Aurora Pujol
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), and CIBERER U759, Center for Biomedical Research on Rare Diseases, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Agatha Schluter
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), and CIBERER U759, Center for Biomedical Research on Rare Diseases, Barcelona, Spain
| | - Gregory A Graf
- Department of Pharmaceutical Sciences, University of Kentucky, Lexington, Kentucky, USA
- Center for Pharmaceutical Research and Innovation, University of Kentucky, Lexington, Kentucky, USA
- Saha Cardiovascular Research Center, University of Kentucky, Lexington, Kentucky, USA
| | - Henry N Ginsberg
- Irving Institute for Clinical and Translational Research, Columbia University College of Physicians and Surgeons, New York, New York, USA
| | - Hetal S Shah
- Joslin Diabetes Center and Harvard Medical School, Boston, Massachusetts, USA
| | - He Gao
- Joslin Diabetes Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Mario-Luca Morieri
- Joslin Diabetes Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Doria
- Joslin Diabetes Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Josyf C Mychaleckyi
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | | | - John B Buse
- Division of Endocrinology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Michael J Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alison A Motsinger-Reif
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
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11
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Graham HT, Rotroff DM, Marvel SW, Buse JB, Havener TM, Wilson AG, Wagner MJ, Motsinger-Reif AA. Incorporating Concomitant Medications into Genome-Wide Analyses for the Study of Complex Disease and Drug Response. Front Genet 2016; 7:138. [PMID: 27775101 PMCID: PMC5013254 DOI: 10.3389/fgene.2016.00138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 07/19/2016] [Indexed: 11/13/2022] Open
Abstract
Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10-7 to p = 1.76 × 10-5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available.
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Affiliation(s)
- Hillary T Graham
- Department of Statistics, North Carolina State University Raleigh, NC, USA
| | - Daniel M Rotroff
- Department of Statistics, North Carolina State UniversityRaleigh, NC, USA; Bioinformatics Research Center, North Carolina State UniversityRaleigh, NC, USA
| | - Skylar W Marvel
- Bioinformatics Research Center, North Carolina State University Raleigh, NC, USA
| | - John B Buse
- Department of Medicine, University of North Carolina School of Medicine Chapel Hill, NC, USA
| | - Tammy M Havener
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Alyson G Wilson
- Department of Statistics, North Carolina State University Raleigh, NC, USA
| | - Michael J Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Alison A Motsinger-Reif
- Department of Statistics, North Carolina State UniversityRaleigh, NC, USA; Bioinformatics Research Center, North Carolina State UniversityRaleigh, NC, USA
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