1
|
Sanchez-Ibarra HE, Reyes-Cortes LM, Jiang XL, Luna-Aguirre CM, Aguirre-Trevino D, Morales-Alvarado IA, Leon-Cachon RB, Lavalle-Gonzalez F, Morcos F, Barrera-Saldaña HA. Genotypic and Phenotypic Factors Influencing Drug Response in Mexican Patients With Type 2 Diabetes Mellitus. Front Pharmacol 2018; 9:320. [PMID: 29681852 PMCID: PMC5898372 DOI: 10.3389/fphar.2018.00320] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 03/20/2018] [Indexed: 12/17/2022] Open
Abstract
The treatment of Type 2 Diabetes Mellitus (T2DM) consists primarily of oral antidiabetic drugs (OADs) that stimulate insulin secretion, such as sulfonylureas (SUs) and reduce hepatic glucose production (e.g., biguanides), among others. The marked inter-individual differences among T2DM patients’ response to these drugs have become an issue on prescribing and dosing efficiently. In this study, fourteen polymorphisms selected from Genome-wide association studies (GWAS) were screened in 495 T2DM Mexican patients previously treated with OADs to find the relationship between the presence of these polymorphisms and response to the OADs. Then, a novel association screening method, based on global probabilities, was used to globally characterize important relationships between the drug response to OADs and genetic and clinical parameters, including polymorphisms, patient information, and type of treatment. Two polymorphisms, ABCC8-Ala1369Ser and KCNJ11-Glu23Lys, showed a significant impact on response to SUs. Heterozygous ABCC8-Ala1369Ser variant (A/C) carriers exhibited a higher response to SUs compared to homozygous ABCC8-Ala1369Ser variant (A/A) carriers (p-value = 0.029) and to homozygous wild-type genotypes (C/C) (p-value = 0.012). The homozygous KCNJ11-Glu23Lys variant (C/C) and wild-type (T/T) genotypes had a lower response to SUs compared to heterozygous (C/T) carriers (p-value = 0.039). The screening of OADs response related genetic and clinical factors could help improve the prescribing and dosing of OADs for T2DM patients and thus contribute to the design of personalized treatments.
Collapse
Affiliation(s)
| | | | - Xian-Li Jiang
- Evolutionary Information Laboratory, Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, United States
| | | | | | | | - Rafael B Leon-Cachon
- Departamento de Ciencias Básicas, Centro de Diagnóstico Molecular y Medicina Personalizada, Vicerrectoría de Ciencias de la Salud, Universidad de Monterrey, Monterrey, Mexico
| | - Fernando Lavalle-Gonzalez
- Servicio de Endocrinología, Hospital Universitario Dr. José E. González, Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Faruck Morcos
- Evolutionary Information Laboratory, Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, United States.,Center for Systems Biology, University of Texas at Dallas, Richardson, TX, United States
| | - Hugo A Barrera-Saldaña
- Molecular Genetics Laboratory, Vitagénesis, S.A. de C.V., Monterrey, Mexico.,Tecnológico de Monterrey, Monterrey, Mexico
| |
Collapse
|
2
|
Dumitrescu L, Diggins KE, Goodloe R, Crawford DC. TESTING POPULATION-SPECIFIC QUANTITATIVE TRAIT ASSOCIATIONS FOR CLINICAL OUTCOME RELEVANCE IN A BIOREPOSITORY LINKED TO ELECTRONIC HEALTH RECORDS: LPA AND MYOCARDIAL INFARCTION IN AFRICAN AMERICANS. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2016; 21:96-107. [PMID: 26776177 PMCID: PMC4720978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Previous candidate gene and genome-wide association studies have identified common genetic variants in LPA associated with the quantitative trait Lp(a), an emerging risk factor for cardiovascular disease. These associations are population-specific and many have not yet been tested for association with the clinical outcome of interest. To fill this gap in knowledge, we accessed the epidemiologic Third National Health and Nutrition Examination Surveys (NHANES III) and BioVU, the Vanderbilt University Medical Center biorepository linked to de-identified electronic health records (EHRs), including billing codes (ICD-9-CM) and clinical notes, to test population-specific Lp(a)-associated variants for an association with myocardial infarction (MI) among African Americans. We performed electronic phenotyping among African Americans in BioVU≥40 years of age using billing codes. At total of 93 cases and 522 controls were identified in NHANES III and 265 cases and 363 controls were identified in BioVU. We tested five known Lp(a)-associated genetic variants (rs1367211, rs41271028, rs6907156, rs10945682, and rs1652507) in both NHANES III and BioVU for association with myocardial infarction. We also tested LPA rs3798220 (I4399M), previously associated with increased levels of Lp(a), MI, and coronary artery disease in European Americans, in BioVU. After meta-analysis, tests of association using logistic regression assuming an additive genetic model revealed no significant associations (p<0.05) for any of the five LPA variants previously associated with Lp(a) levels in African Americans. Also, I4399M rs3798220 was not associated with MI in African Americans (odds ratio = 0.51; 95% confidence interval: 0.16 - 1.65; p=0.26) despite strong, replicated associations with MI and coronary artery disease in European American genome-wide association studies. These data highlight the challenges in translating quantitative trait associations to clinical outcomes in diverse populations using large epidemiologic and clinic-based collections as envisioned for the Precision Medicine Initiative.
Collapse
Affiliation(s)
- Logan Dumitrescu
- Center for Human Genetics Research, Vanderbilt University, 519 Light Hall, 2215 Garland Avenue, Nashville, TN 37232, USA
| | - Kirsten E. Diggins
- Cancer Biology, Vanderbilt University, 742 Preston Research Building, 2220 Pierce Avenue, Nashville, TN 37232, USA
| | - Robert Goodloe
- Center for Human Genetics Research, Vanderbilt University, 519 Light Hall, 2215 Garland Avenue, Nashville, TN 37232, USA
| | - Dana C. Crawford
- Institute for Computational Biology, Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road, Suite 2527, Cleveland, OH 44106, USA
| |
Collapse
|
3
|
Fan J, Liu H. Statistical analysis of big data on pharmacogenomics. Adv Drug Deliv Rev 2013; 65:987-1000. [PMID: 23602905 PMCID: PMC3701723 DOI: 10.1016/j.addr.2013.04.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Revised: 04/07/2013] [Accepted: 04/10/2013] [Indexed: 01/29/2023]
Abstract
This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed.
Collapse
Affiliation(s)
- Jianqing Fan
- Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA.
| | | |
Collapse
|
4
|
Turner SD, Berg RL, Linneman JG, Peissig PL, Crawford DC, Denny JC, Roden DM, McCarty CA, Ritchie MD, Wilke RA. Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS One 2011; 6:e19586. [PMID: 21589926 PMCID: PMC3092760 DOI: 10.1371/journal.pone.0019586] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2010] [Accepted: 04/01/2011] [Indexed: 11/18/2022] Open
Abstract
Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h(2)∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.
Collapse
Affiliation(s)
- Stephen D. Turner
- Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Richard L. Berg
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - James G. Linneman
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - Peggy L. Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - Dana C. Crawford
- Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Joshua C. Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Dan M. Roden
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Catherine A. McCarty
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, United States of America
| | - Marylyn D. Ritchie
- Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Russell A. Wilke
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| |
Collapse
|
5
|
Grady BJ, Ritchie MD. Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis. CURRENT PHARMACOGENOMICS AND PERSONALIZED MEDICINE 2011; 9:41-66. [PMID: 21887206 PMCID: PMC3163263 DOI: 10.2174/187569211794728805] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Research in human genetics and genetic epidemiology has grown significantly over the previous decade, particularly in the field of pharmacogenomics. Pharmacogenomics presents an opportunity for rapid translation of associated genetic polymorphisms into diagnostic measures or tests to guide therapy as part of a move towards personalized medicine. Expansion in genotyping technology has cleared the way for widespread use of whole-genome genotyping in the effort to identify novel biology and new genetic markers associated with pharmacokinetic and pharmacodynamic endpoints. With new technology and methodology regularly becoming available for use in genetic studies, a discussion on the application of such tools becomes necessary. In particular, quality control criteria have evolved with the use of GWAS as we have come to understand potential systematic errors which can be introduced into the data during genotyping. There have been several replicated pharmacogenomic associations, some of which have moved to the clinic to enact change in treatment decisions. These examples of translation illustrate the strength of evidence necessary to successfully and effectively translate a genetic discovery. In this review, the design of pharmacogenomic association studies is examined with the goal of optimizing the impact and utility of this research. Issues of ascertainment, genotyping, quality control, analysis and interpretation are considered.
Collapse
Affiliation(s)
- Benjamin J. Grady
- Department of Molecular Physiology & Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA
| | - Marylyn D. Ritchie
- Department of Molecular Physiology & Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|