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Lin E, Lane HY. Genome-wide association studies in pharmacogenomics of antidepressants. Pharmacogenomics 2016; 16:555-66. [PMID: 25916525 DOI: 10.2217/pgs.15.5] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Doctors must prescribe antidepressants based on educated guesses due to the fact that it is unmanageable to predict the effectiveness of any particular antidepressant in an individual patient. With the recent advent of scientific research, the genome-wide association study (GWAS) is extensively employed to analyze hundreds of thousands of single nucleotide polymorphisms by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been utilized to investigate the determinants of antidepressant response to therapy. In this study, we reviewed GWAS studies, their limitations and future directions with respect to the pharmacogenomics of antidepressants in MDD.
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Affiliation(s)
- Eugene Lin
- Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan
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Lane HY, Tsai GE, Lin E. Assessing Gene-Gene Interactions in Pharmacogenomics. Mol Diagn Ther 2012; 16:15-27. [DOI: 10.1007/bf03256426] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Wen MJ, Lin CJ, Hung YJ, Pei D, Kuo SW, Hsieh CH. Association Study Between Apolipoprotein E Gene Polymorphism and Diabetic Nephropathy in a Taiwanese Population. Genet Test Mol Biomarkers 2011; 15:685-9. [DOI: 10.1089/gtmb.2010.0201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
- Min-Jie Wen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, Taipei, Taiwan
| | - Chin-Jung Lin
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, Taipei, Taiwan
| | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, Taipei, Taiwan
| | - Dee Pei
- Division of Endocrinology, Cardinal Tien Hospital, Taipei County, Taiwan
| | - Shi-Wen Kuo
- Division of Endocrinology and Metabolism, Buddhist Xindian Tzu Chi General Hospital, Taipei, Taiwan
| | - Chang-Hsun Hsieh
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, Taipei, Taiwan
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Hevezi PA, Tom E, Wilson K, Lambert P, Gutierrez-Reyes G, Kershenobich D, Zlotnik A. Gene expression patterns in livers of Hispanic patients infected with hepatitis C virus. Autoimmunity 2011; 44:532-42. [PMID: 21864061 DOI: 10.3109/08916934.2011.592881] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We report a gene expression study aimed at the identification of genes differentially expressed in the livers of Hispanic patients infected with hepatitis C virus (HCV). Six uninfected controls were compared with 14 HCV(+) patients in which the liver biopsies were obtained at the time of diagnosis. Among the latter, five patients were also analyzed 4 weeks after the onset of standard anti-HCV therapy (pegylated interferon-α + ribavirin). We identified many genes up- or down-regulated by the infection with HCV in the human livers. When these genes were subjected to pathway analysis, several prominent pathways were revealed including many interferon (IFN)-inducible pathways as well as immune cell trafficking, inflammation, anti-microbial responses, and even cancer. We detected expression of many genes that have previously been associated with HCV infection, as well as several novel genes including CD47. The genes induced by HCV infection showed large expression changes, whereas the genes induced by the IFN-α combination therapy were relatively few (including MX2, ORMDL3, GPAM, KOPX18, TMEM56, and HBP1) and they reflected relatively small expression changes. This is the first study to identify changes in gene expression in livers of HCV(+) Hispanic patients and the first to identify genes induced by anti-HCV combination therapy in the human liver.
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Affiliation(s)
- Peter A Hevezi
- FACET Biotech, 1500 Seaport Blvd, Redwood City, CA 94063, USA
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Porto Neto LR, Bunch RJ, Harrison BE, Barendse W. DNA variation in the gene ELTD1 is associated with tick burden in cattle. Anim Genet 2010; 42:50-5. [PMID: 20880337 DOI: 10.1111/j.1365-2052.2010.02120.x] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ticks and tick-born diseases are major constraints on cattle production in tropical and subtropical regions in the world. Previously, we identified single nucleotide polymorphisms (SNPs) associated with tick resistance on bovine chromosome 3 at approximately 70 Mb. In this study, we genotyped a dairy (n = 1133) and a beef (n = 774) sample to confirm the association of the intronic SNP rs29019303 and its gene (ELTD1) with tick burden. We genotyped 18 additional SNPs in a region of 181 kb and found that rs29019303 was significantly (P < 0.05) associated with tick burden in both samples with the same favourable allele. A second SNP in this same genomic region was also significantly associated with tick burden in each sample. The associations using haplotypes were stronger than for single markers, including a haplotype of nine tag SNPs that was highly significantly (P = 0.0008) associated with tick counts in the dairy animals. This haplotype and two others were significant after Bonferroni correction for multiple testing. The estimated size of the effects was close to 0.9% of the residual variance in both samples tested.
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Affiliation(s)
- L R Porto Neto
- Cooperative Research Centre for Beef Genetic Technologies, CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia
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Hsiao TJ, Wu LSH, Hwang Y, Huang SY, Lin E. Effect of the common -866G/A polymorphism of the uncoupling protein 2 gene on weight loss and body composition under sibutramine therapy in an obese Taiwanese population. Mol Diagn Ther 2010; 14:101-6. [PMID: 20359253 DOI: 10.1007/bf03256359] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Sibutramine, a serotonin and norepinephrine reuptake inhibitor, is used as an anti-obesity drug. Several pharmacogenetic studies have shown correlations between sibutramine effects and genetic variants, such as the 825C/T (rs5443) single nucleotide polymorphism (SNP) in the guanine nucleotide binding protein beta polypeptide 3 (GNB3) gene. OBJECTIVE In this study, our goal was to investigate whether a common SNP, -866G/A (rs659366), in the uncoupling protein 2 (UCP2) gene could influence weight reduction and body composition under sibutramine therapy in an obese Taiwanese population. METHODS The study included 131 obese patients, 44 in the placebo group and 87 in the sibutramine group. We assessed the measures of weight loss and body fat reduction at the end of a 12-week treatment period by analysis of covariance (ANCOVA) models using gender, baseline weight, and body fat percentage at baseline as covariates. RESULTS AND CONCLUSION By comparing the placebo and sibutramine groups with ANCOVA, our data showed a strong effect of sibutramine on weight loss in the combined UCP2 -866 AA + GA genotype groups (p < 0.001). Similarly, a strong effect of sibutramine on body fat percentage loss was found for individuals with the AA or GA genotypes (p < 0.001). In contrast, sibutramine had no significant effect on weight loss (p = 0.063) or body fat percentage loss (p = 0.194) for individuals with the wild-type GG genotype, compared with the placebo group of the same genotype. Moreover, a potential gene-gene interaction between UCP2 and GNB3 was identified by multiple linear regression models for the weight loss (p < 0.001) and for the percent fat loss (p = 0.031) in response to sibutramine. The results suggest that the UCP2 gene may contribute to weight loss and fat change in response to sibutramine therapy in obese Taiwanese patients.
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Affiliation(s)
- Tun-Jen Hsiao
- College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan
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Wang CH, Hwang Y, Lin E. Pharmacogenomics of chronic hepatitis C therapy with genome-wide association studies. J Exp Pharmacol 2010; 2:73-82. [PMID: 27186094 PMCID: PMC4863289 DOI: 10.2147/jep.s8655] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Chronic hepatitis C (CHC) is a liver disease characterized by infection with the hepatitis C virus (HCV) persisting for more than six months. Patients with CHC often stop pursuing the pegylated interferon (peg-IFN) and ribavirin (RBV) treatment because of the high cost and associated adverse effects. Therefore, it is highly desirable, both clinically and economically, to establish the determinants of response to distinguish responders from nonresponders, and to predict the possible outcomes of the peg-IFN and RBV treatments. The aim of this study was to review recent data on the pharmacogenomics of the drug efficacy of IFN in CHC patients. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN therapeutic response. In the recent advent of scientific research, the genome-wide association study (GWAS), which is an alternative to the candidate-gene approach, is widely utilized to examine hundreds of thousands of SNPs by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been employed to study the determinants of HCV's response to therapy. Several recent findings have demonstrated that some SNPs in the interleukin 28B gene are closely associated with IFN responsiveness. These results promise to lead to mechanistic findings related to IFN responsiveness in this disease, and will probably have major contributions for individualized medicine and therapeutic decision making.
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Affiliation(s)
- Chun-Hsiang Wang
- Department of Hepatogastroenterology, Tainan Municipal Hospital, Tainan, Taiwan
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Ke WS, Hwang Y, Lin E. Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms. Adv Appl Bioinform Chem 2010; 3:39-44. [PMID: 21918625 PMCID: PMC3170005 DOI: 10.2147/aabc.s8656] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics.
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Affiliation(s)
- Wan-Sheng Ke
- Department of Internal Medicine, Kuang Tien General Hospital, Taichung County, Taiwan
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Lin E, Hong CJ, Hwang JP, Liou YJ, Yang CH, Cheng D, Tsai SJ. Gene–Gene Interactions of the Brain-Derived Neurotrophic-Factor and Neurotrophic Tyrosine Kinase Receptor 2 Genes in Geriatric Depression. Rejuvenation Res 2009; 12:387-93. [PMID: 20014955 DOI: 10.1089/rej.2009.0871] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Affiliation(s)
| | - Chen-Jee Hong
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Jen-Ping Hwang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
| | - Ying-Jay Liou
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
| | - Chen-Hong Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
| | - Daniel Cheng
- Department of Microbiology, Immunology, and Molecular Genetics, University of California–Los Angeles, Los Angeles, California
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
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Lin E, Chen PS, Chang HH, Gean PW, Tsai HC, Yang YK, Lu RB. Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2009; 33:1167-72. [PMID: 19560507 DOI: 10.1016/j.pnpbp.2009.06.015] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2009] [Revised: 06/16/2009] [Accepted: 06/17/2009] [Indexed: 11/28/2022]
Abstract
BACKGROUND Four serotonin-related genes including guanine nucleotide binding protein beta polypeptide 3 (GNB3), 5-hydroxytryptamine receptor 1A (HTR1A; serotonin receptor 1A), 5-hydroxytryptamine receptor 2A (HTR2A; serotonin receptor 2A), and solute carrier family 6 member 4 (SLC6A4; serotonin neurotransmitter transporter) have been suggested to be candidate genes for influencing antidepressant treatment outcome. The aim of this study was to explore whether interaction among these genes could contribute to the pharmacogenomics of short-term antidepressant response in a Taiwanese population with major depressive disorder (MDD). METHODS Included in this study were 101 MDD patients who were treated with antidepressants, 35 of whom were rapid responders and 66 non-responders after 2weeks of treatment. We genotyped four single nucleotide polymorphisms (SNPs), including GNB3 rs5443 (C825T), HTR1A rs6295 (C-1019G), HTR2A rs6311 (T102C), and SLC6A4 rs25533, and employed the generalized multifactor dimensionality reduction (GMDR) method to investigate gene-gene interactions. RESULTS Single-locus analyses showed the GNB3 rs5443 polymorphism to be associated with short-term antidepressant treatment outcome (P-value=0.029). We did not correct for multiple testing in these multiple exploratory analyses. Finally, the GMDR approach identified a significant gene-gene interaction (P-value=0.025) involving GNB3 and HTR2A, as well as a significant 3-locus model (P-value=0.015) among GNB3, HTR2A, and SLC6A4. CONCLUSIONS These results support the hypothesis that GNB3, HTR2A, and SLC6A4 may play a role in the outcome of short-term antidepressant treatment for MDD in an interactive manner. Future research with independent replication using large sample sizes is needed to confirm the functions of the candidate genes identified in this study as being involved in short-term antidepressant treatment response.
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Affiliation(s)
- Eugene Lin
- Vita Genomics, Inc, Wugu Shiang, Taipei, Taiwan
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Huang LC, Hsu SY, Lin E. A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data. J Transl Med 2009; 7:81. [PMID: 19772600 PMCID: PMC2765429 DOI: 10.1186/1479-5876-7-81] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2009] [Accepted: 09/22/2009] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs). METHODS We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method. RESULTS The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs. CONCLUSION We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.
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Affiliation(s)
- Lung-Cheng Huang
- Department of Psychiatry, National Taiwan University Hospital Yun-Lin Branch, Taiwan
- Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Sen-Yen Hsu
- Department of Psychiatry, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | - Eugene Lin
- Vita Genomics, Inc, 7 Fl, No 6, Sec 1, Jung-Shing Road, Wugu Shiang, Taipei, Taiwan
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Lin E, Pei D, Huang YJ, Hsieh CH, Wu LSH. Gene-Gene Interactions Among Genetic Variants from Obesity Candidate Genes for Nonobese and Obese Populations in Type 2 Diabetes. Genet Test Mol Biomarkers 2009; 13:485-93. [DOI: 10.1089/gtmb.2008.0145] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Eugene Lin
- Bioinformatics Division, Vita Genomics, Inc., Taipei County, Taiwan
| | - Dee Pei
- Division of Endocrinology and Metabolism, Cardinal Tien Hospital, Taipei County, Taiwan
| | - Yi-Jen Huang
- Division of Endocrinology and Metabolism, Tri-Service General Hospital, Taipei, Taiwan
| | - Chang-Hsun Hsieh
- Division of Endocrinology and Metabolism, Tri-Service General Hospital, Taipei, Taiwan
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Wu LSH, Hsieh CH, Pei D, Hung YJ, Kuo SW, Lin E. Association and interaction analyses of genetic variants in ADIPOQ, ENPP1, GHSR, PPAR and TCF7L2 genes for diabetic nephropathy in a Taiwanese population with type 2 diabetes. Nephrol Dial Transplant 2009; 24:3360-6. [DOI: 10.1093/ndt/gfp271] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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Lin E, Hsu SY. A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome. Pharmacogenomics 2009; 10:35-42. [PMID: 19102713 DOI: 10.2217/14622416.10.1.35] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
INTRODUCTION In the study of genomics, it is essential to address gene-gene and gene-environment interactions for describing the complex traits that involves disease-related mechanisms. In this work, our goal is to detect gene-gene and gene-environment interactions resulting from the analysis of chronic fatigue syndrome patients' genetic and demographic factors including SNPs, age, gender and BMI. MATERIALS & METHODS We employed the dataset that was original to the previous study by the Centers for Disease Control and Prevention Chronic Fatigue Syndrome Research Group. To investigate gene-gene and gene-environment interactions, we implemented a Bayesian based method for identifying significant interactions between factors. Here, we employed a two-stage Bayesian variable selection methodology based on Markov Chain Monte Carlo approaches. RESULTS By applying our Bayesian based approach, NR3C1 was found in the significant two-locus gene-gene effect model, as well as in the significant two-factor gene-environment effect model. Furthermore, a significant gene-environment interaction was identified between NR3C1 and gender. These results support the hypothesis that NR3C1 and gender may play a role in biological mechanisms associated with chronic fatigue syndrome. CONCLUSION We demonstrated that our Bayesian based approach is a promising method to assess the gene-gene and gene-environment interactions in chronic fatigue syndrome patients by using genetic factors, such as SNPs, and demographic factors such as age, gender and BMI.
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Affiliation(s)
- Eugene Lin
- Vita Genomics, Inc., Jung-Shing Road, Wugu Shiang, Taipei, Taiwan.
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Calzolari D, Bruschi S, Coquin L, Schofield J, Feala JD, Reed JC, McCulloch AD, Paternostro G. Search algorithms as a framework for the optimization of drug combinations. PLoS Comput Biol 2008; 4:e1000249. [PMID: 19112483 PMCID: PMC2590660 DOI: 10.1371/journal.pcbi.1000249] [Citation(s) in RCA: 73] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2007] [Accepted: 11/11/2008] [Indexed: 12/13/2022] Open
Abstract
Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.
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Affiliation(s)
- Diego Calzolari
- Burnham Institute for Medical Research, La Jolla, California, United
States of America
| | - Stefania Bruschi
- Burnham Institute for Medical Research, La Jolla, California, United
States of America
| | - Laurence Coquin
- Burnham Institute for Medical Research, La Jolla, California, United
States of America
| | - Jennifer Schofield
- Burnham Institute for Medical Research, La Jolla, California, United
States of America
| | - Jacob D. Feala
- Department of Bioengineering, University of California San Diego, La
Jolla, California, United States of America
| | - John C. Reed
- Burnham Institute for Medical Research, La Jolla, California, United
States of America
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California San Diego, La
Jolla, California, United States of America
| | - Giovanni Paternostro
- Burnham Institute for Medical Research, La Jolla, California, United
States of America
- Department of Bioengineering, University of California San Diego, La
Jolla, California, United States of America
- * E-mail:
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Lin E, Chen PS, Huang LC, Hsu SY. Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2008; 1:1-6. [PMID: 23226029 PMCID: PMC3513194 DOI: 10.2147/pgpm.s4116] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Major depressive disorder (MDD) is one of the most common mental disorders worldwide. Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. A common SNP in the brain-derived neurotrophic factor (BDNF) gene, a methionine (Met) substitution for valine (Val) at codon 66 (Val66Met), is a candidate SNP for influencing antidepressant treatment outcome. In this study, our goal was to determine the relationship between the Val66Met polymorphism in the BDNF gene and the rapid antidepressant response to venlafaxine in a Taiwanese population with MDD. Overall, the BDNF Val66Met polymorphism was found not to be associated with short-term venlafaxine treatment outcome. However, the BDNF Val66Met polymorphism showed a trend to be associated with rapid venlafaxine treatment response in female patients. Future research with independent replication in large sample sizes is needed to confirm the role of the BDNF Val66Met polymorphism identified in this study.
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Affiliation(s)
- Eugene Lin
- Vita Genomics, Inc., Wugu Shiang, Taipei, Taiwan; ; These authors contributed equally to this work
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Lin E, Hwang Y. A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy. Mol Diagn Ther 2008; 12:219-23. [PMID: 18652518 DOI: 10.1007/bf03256287] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND Interferon-alpha (IFNalpha) in combination with ribavirin can be used for the treatment of patients with chronic hepatitis C. This therapeutic approach achieves an overall sustained response rate of approximately 40%, but treatment takes 6-12 months and patients often experience significant adverse reactions. OBJECTIVE We aim to develop a tool to distinguish potential responders from nonresponders prior to initiation of IFNalpha-ribavirin treatment. METHODS Using single nucleotide polymorphisms (SNPs) and viral genotype, we applied the support vector machine (SVM) algorithm to build a tool to predict responsiveness to IFNalpha-ribavirin combination therapy. Furthermore, we utilized the SVM algorithm with the recursive feature elimination method to identify a subset of factors that are significantly more influential than the others. RESULTS AND CONCLUSION The SVM model is a promising method for inferring responsiveness to IFNalpha dealing with the complex nonlinear relationship between factors (such as SNPs and viral genotype) and successful therapy. In this study, we demonstrate that our tool may allow patients and doctors to make more informed decisions by analyzing host SNP and viral genotype information.
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Lin E, Huang LC. Identification of significant genes in genomics using Bayesian variable selection methods. Adv Appl Bioinform Chem 2008; 1:13-8. [PMID: 21918603 PMCID: PMC3169938 DOI: 10.2147/aabc.s3624] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that are significantly more influential than the others. We employed the framework of variable selection and a Gibbs sampling based technique to identify significant genes. The proposed approach was applied to a genomics study for persons with chronic fatigue syndrome. Our studies show that the proposed Bayesian methodology is effective for deriving models for genomic studies and for providing information on significant genes.
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Affiliation(s)
- Eugene Lin
- Vita Genomics, Inc., Wugu Shiang, Taipei, Taiwan
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Abstract
Major depressive disorder is one of the most common psychiatric disorders worldwide. No single antidepressant has been shown to be more effective than any other in lifting depression, and the effectiveness of any particular antidepressant in an individual is difficult to predict; therefore, doctors must prescribe antidepressants based on educated guesses. SNPs can be used in clinical association studies to determine the contribution of genes to drug efficacy. Evidence is accumulating to suggest that the efficacy of antidepressants results from the combined effects of a number of genetic variants, such as SNPs. Although there are not enough data currently available to prove this hypothesis, an increasing number of genetic variants associated with antidepressant response are being discovered. In this article, we review the pharmacogenomics of the drug efficacy of antidepressants in major depressive disorder. First, we survey the SNPs and genes identified as genetic markers that are correlated and associated with the drug efficacy of antidepressants in the Sequenced Treatment Alternatives for Depression (STAR*D) study. Second, we investigate candidate genes that have been suggested as contributing to treatment-emergent suicidal ideation during the course of antidepressant treatment in the STAR*D study. Third, we briefly describe the pharmacokinetic genes examined in the STAR*D study, and finally, we summarize the limitations with respect to the pharmacogenomics studies in the STAR*D study. Future research with independent replication in large sample sizes is needed to confirm the role of the candidate genes identified in the STAR*D study in antidepressant treatment response.
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Affiliation(s)
- Eugene Lin
- Vita Genomics, Inc, 7 Fl., No. 6, Sec. 1, Jung-Shing Road, Wugu Shiang, Taipei, Taiwan
| | - Po See Chen
- Department of Psychiatry, Hospital & College of Medicine, National Cheng Kung University, Tainan, Taiwan
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