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Published in: Molecular Diagnosis & Therapy 1/2012

01-02-2012 | Review Article

Assessing Gene-Gene Interactions in Pharmacogenomics

Authors: Hsien-Yuan Lane, Guochuan E. Tsai, Dr Eugene Lin

Published in: Molecular Diagnosis & Therapy | Issue 1/2012

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Abstract

In pharmacogenomics studies, gene-gene interactions play an important role in characterizing a trait that involves complex pharmacokinetic and pharmacodynamic mechanisms, particularly when each involved feature only demonstrates a minor effect. In addition to the candidate gene approach, genome-wide association studies (GWAS) are widely utilized to identify common variants that are associated with treatment response. In the wake of recent advances in scientific research, a paradigm shift from GWAS to whole-genome sequencing is expected, because of the reduced cost and the increased throughput of next-generation sequencing technologies. This review first outlines several promising methods for addressing gene-gene interactions in pharmacogenomics studies. We then summarize some candidate gene studies for various treatments with consideration of gene-gene interactions. Furthermore, we give a brief overview for the pharmacogenomics studies with the GWAS approach and describe the limitations of these GWAS in terms of gene-gene interactions. Future research in translational medicine promises to lead to mechanistic findings related to drug responsiveness in light of complex gene-gene interactions and will probably make major contributions to individualized medicine and therapeutic decision-making.
Literature
1.
go back to reference Bell JT, Wallace C, Dobson R, et al. Two-dimensional genome-scan identifies novel epistatic loci for essential hypertension. Hum Mol Genet 2006; 15:1365–74PubMedCrossRef Bell JT, Wallace C, Dobson R, et al. Two-dimensional genome-scan identifies novel epistatic loci for essential hypertension. Hum Mol Genet 2006; 15:1365–74PubMedCrossRef
2.
go back to reference Carlborg O, Haley CS. Epistasis: too often neglected in complex trait studies. Nature 2004; 5: 618–25 Carlborg O, Haley CS. Epistasis: too often neglected in complex trait studies. Nature 2004; 5: 618–25
3.
go back to reference Lin E, Hwang Y, Liang KH, et al. Pattern-recognition techniques with haplotype analysis in pharmacogenomics. Pharmacogenomics 2007; 8: 75–83PubMedCrossRef Lin E, Hwang Y, Liang KH, et al. Pattern-recognition techniques with haplotype analysis in pharmacogenomics. Pharmacogenomics 2007; 8: 75–83PubMedCrossRef
4.
go back to reference Lin E, Hwang Y, Chen EY. Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C. Pharmacogenomics 2007; 8: 1327–35PubMedCrossRef Lin E, Hwang Y, Chen EY. Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C. Pharmacogenomics 2007; 8: 1327–35PubMedCrossRef
5.
go back to reference Lin E, Hsu SY. A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome. Pharmacogenomics 2009; 10: 35–42PubMedCrossRef Lin E, Hsu SY. A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome. Pharmacogenomics 2009; 10: 35–42PubMedCrossRef
6.
go back to reference Johnson AD, O’Donnell CJ. An open access database of genome-wide association results. BMC Med Genet 2009; 10: 6PubMedCrossRef Johnson AD, O’Donnell CJ. An open access database of genome-wide association results. BMC Med Genet 2009; 10: 6PubMedCrossRef
7.
go back to reference Christensen K, Murray JC. What genome-wide association studies can do for medicine. N Engl J Med 2007; 356: 1094–7PubMedCrossRef Christensen K, Murray JC. What genome-wide association studies can do for medicine. N Engl J Med 2007; 356: 1094–7PubMedCrossRef
8.
go back to reference Need AC, Goldstein DB. Whole genome association studies in complex diseases: where do we stand? Dialogues Clin Neurosci 2010; 12: 37–46PubMed Need AC, Goldstein DB. Whole genome association studies in complex diseases: where do we stand? Dialogues Clin Neurosci 2010; 12: 37–46PubMed
9.
go back to reference Wheeler DA, Srinivasan M, Egholm M, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 2008; 452: 872–6PubMedCrossRef Wheeler DA, Srinivasan M, Egholm M, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 2008; 452: 872–6PubMedCrossRef
11.
go back to reference Tucker T, Marra M, Friedman JM. Massively parallel sequencing: the next big thing in genetic medicine. Am J Hum Genet 2009; 85: 142–54PubMedCrossRef Tucker T, Marra M, Friedman JM. Massively parallel sequencing: the next big thing in genetic medicine. Am J Hum Genet 2009; 85: 142–54PubMedCrossRef
12.
go back to reference McKinney BA, Reif DM, Ritchie MD, et al. Machine learning for detecting gene-gene interactions: a review. Appl Bioinformatics 2006; 5: 77–88PubMedCrossRef McKinney BA, Reif DM, Ritchie MD, et al. Machine learning for detecting gene-gene interactions: a review. Appl Bioinformatics 2006; 5: 77–88PubMedCrossRef
13.
go back to reference Motsinger AA, Ritchie MD, Reif DM. Novel methods for detecting epistasis in pharmacogenomics studies. Pharmacogenomics 2007; 8: 1229–41PubMedCrossRef Motsinger AA, Ritchie MD, Reif DM. Novel methods for detecting epistasis in pharmacogenomics studies. Pharmacogenomics 2007; 8: 1229–41PubMedCrossRef
14.
go back to reference Phillips PC. Epistasis: the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 2008; 9: 855–67PubMedCrossRef Phillips PC. Epistasis: the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 2008; 9: 855–67PubMedCrossRef
15.
go back to reference Cordell HJ. Genome-wide association studies: detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 2009; 10: 392–404PubMedCrossRef Cordell HJ. Genome-wide association studies: detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 2009; 10: 392–404PubMedCrossRef
16.
go back to reference Moore JH, Williams SM. Epistasis and its implications for personal genetics. Am J Hum Genet 2009; 85: 309–20PubMedCrossRef Moore JH, Williams SM. Epistasis and its implications for personal genetics. Am J Hum Genet 2009; 85: 309–20PubMedCrossRef
17.
go back to reference Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–75PubMedCrossRef Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–75PubMedCrossRef
18.
go back to reference Bellman R. Adaptive control processes. Princeton (NJ): Princeton University Press, 1961 Bellman R. Adaptive control processes. Princeton (NJ): Princeton University Press, 1961
19.
go back to reference Moore JH, Williams SM. New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002; 34: 88–95PubMedCrossRef Moore JH, Williams SM. New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002; 34: 88–95PubMedCrossRef
20.
go back to reference Thornton-Wells TA, Moore JH, Haines JL. Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data. BMC Bioinformatics 2006; 7: 204–21PubMedCrossRef Thornton-Wells TA, Moore JH, Haines JL. Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data. BMC Bioinformatics 2006; 7: 204–21PubMedCrossRef
21.
go back to reference Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 2008; 40: 695–701PubMedCrossRef Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 2008; 40: 695–701PubMedCrossRef
22.
go back to reference Ma DQ, Whitehead PL, Menold MM, et al. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet 2005; 77: 377–88PubMedCrossRef Ma DQ, Whitehead PL, Menold MM, et al. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet 2005; 77: 377–88PubMedCrossRef
23.
go back to reference Ritchie MD, Hahn LW, Roodi N, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001; 69: 138–47PubMedCrossRef Ritchie MD, Hahn LW, Roodi N, et al. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001; 69: 138–47PubMedCrossRef
24.
go back to reference Ritchie MD, Motsinger AA. Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies. Pharmacogenomics 2005; 6: 823–34PubMedCrossRef Ritchie MD, Motsinger AA. Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies. Pharmacogenomics 2005; 6: 823–34PubMedCrossRef
25.
go back to reference Lin E, Chen PS, Chang HH, et al. Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2009; 33: 1167–72PubMedCrossRef Lin E, Chen PS, Chang HH, et al. Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2009; 33: 1167–72PubMedCrossRef
26.
go back to reference Li MD, Lou XY, Chen G, et al. Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence. Biol Psychiatry 2008; 64: 951–7PubMedCrossRef Li MD, Lou XY, Chen G, et al. Gene-gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence. Biol Psychiatry 2008; 64: 951–7PubMedCrossRef
27.
go back to reference Lou XY, Chen GB, Yan L, et al. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 2007; 80: 1125–37PubMedCrossRef Lou XY, Chen GB, Yan L, et al. A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 2007; 80: 1125–37PubMedCrossRef
28.
go back to reference Jakobsdottir J, Conley YP, Weeks DE, et al. C2 and CFB genes in age-related maculopathy and joint action with CFH and LOC387715 genes. PLoS One 2008; 3: e2199PubMedCrossRef Jakobsdottir J, Conley YP, Weeks DE, et al. C2 and CFB genes in age-related maculopathy and joint action with CFH and LOC387715 genes. PLoS One 2008; 3: e2199PubMedCrossRef
29.
go back to reference Chan IH, Tang NL, Leung TF, et al. Study of gene-gene interactions for endophenotypic quantitative traits in Chinese asthmatic children. Allergy 2008; 63: 1031–9PubMedCrossRef Chan IH, Tang NL, Leung TF, et al. Study of gene-gene interactions for endophenotypic quantitative traits in Chinese asthmatic children. Allergy 2008; 63: 1031–9PubMedCrossRef
30.
go back to reference Lin E, Lin CG, Wang JY, et al. Gene-gene interactions among genetic variants from seven candidate genes with pediatric asthma in a Taiwanese population. Curr Topics Genet 2008; 3: 83–8 Lin E, Lin CG, Wang JY, et al. Gene-gene interactions among genetic variants from seven candidate genes with pediatric asthma in a Taiwanese population. Curr Topics Genet 2008; 3: 83–8
31.
go back to reference Liu J, Sun K, Bai Y, et al. Association of three-gene interaction among MTHFR, ALOX5AP and NOTCH3 with thrombotic stroke: a multicenter case-control study. Hum Genet 2009; 125: 649–56PubMedCrossRef Liu J, Sun K, Bai Y, et al. Association of three-gene interaction among MTHFR, ALOX5AP and NOTCH3 with thrombotic stroke: a multicenter case-control study. Hum Genet 2009; 125: 649–56PubMedCrossRef
32.
go back to reference Lin E, Pei D, Huang YJ, et al. 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: 485–93PubMedCrossRef Lin E, Pei D, Huang YJ, et al. 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: 485–93PubMedCrossRef
33.
go back to reference Henckaerts L, Van Steen K, Verstreken I, et al. Genetic risk profiling and prediction of disease course in Crohn’s disease patients. Clin Gastroenterol Hepatol 2009; 7: 972–80PubMedCrossRef Henckaerts L, Van Steen K, Verstreken I, et al. Genetic risk profiling and prediction of disease course in Crohn’s disease patients. Clin Gastroenterol Hepatol 2009; 7: 972–80PubMedCrossRef
34.
go back to reference Wu LS, Hsieh CH, Pei D, et al. Association and interaction analyses of genetic variants in ADIPOQ, ENPP1, GHSR, PPARgamma and TCF7L2 genes for diabetic nephropathy in a Taiwanese population with type 2 diabetes. Nephrol Dial Transplant 2009; 24: 3360–6PubMedCrossRef Wu LS, Hsieh CH, Pei D, et al. Association and interaction analyses of genetic variants in ADIPOQ, ENPP1, GHSR, PPARgamma and TCF7L2 genes for diabetic nephropathy in a Taiwanese population with type 2 diabetes. Nephrol Dial Transplant 2009; 24: 3360–6PubMedCrossRef
35.
go back to reference Lin E, Hong CJ, Hwang JP, et al. Gene-gene interactions of the brain-derived neurotrophic-factor and neurotrophic tyrosine kinase receptor 2 genes in geriatric depression. Rejuvenation Res 2009; 12: 387–93PubMedCrossRef Lin E, Hong CJ, Hwang JP, et al. Gene-gene interactions of the brain-derived neurotrophic-factor and neurotrophic tyrosine kinase receptor 2 genes in geriatric depression. Rejuvenation Res 2009; 12: 387–93PubMedCrossRef
36.
go back to reference Neuman RJ, Wasson J, Atzmon G, et al. Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population. PLoS One 2010; 5: e9903PubMedCrossRef Neuman RJ, Wasson J, Atzmon G, et al. Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population. PLoS One 2010; 5: e9903PubMedCrossRef
37.
go back to reference Pae CU, Drago A, Patkar AA, et al. Epistasis between a set of variations located in the TAAR6 and HSP-70 genes toward schizophrenia and response to antipsychotic treatment. Eur Neuropsychopharmacol 2009; 19: 806–11PubMedCrossRef Pae CU, Drago A, Patkar AA, et al. Epistasis between a set of variations located in the TAAR6 and HSP-70 genes toward schizophrenia and response to antipsychotic treatment. Eur Neuropsychopharmacol 2009; 19: 806–11PubMedCrossRef
38.
go back to reference Du Y, Wan YJ. The interaction of reward genes with environmental factors in contribution to alcoholism in Mexican Americans. Alcohol Clin Exp Res 2009; 33: 2103–12PubMedCrossRef Du Y, Wan YJ. The interaction of reward genes with environmental factors in contribution to alcoholism in Mexican Americans. Alcohol Clin Exp Res 2009; 33: 2103–12PubMedCrossRef
39.
go back to reference Beretta L, Santaniello A, van Riel PL, et al. Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data. BMC Bioinformatics 2010; 11: 416PubMedCrossRef Beretta L, Santaniello A, van Riel PL, et al. Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data. BMC Bioinformatics 2010; 11: 416PubMedCrossRef
40.
go back to reference Lin E, Hwang Y, Wang SC, et al. An artificial neural network approach to the drug efficacy of interferon treatments. Pharmacogenomics 2006; 7: 1017–24PubMedCrossRef Lin E, Hwang Y, Wang SC, et al. An artificial neural network approach to the drug efficacy of interferon treatments. Pharmacogenomics 2006; 7: 1017–24PubMedCrossRef
41.
go back to reference Kung SY, Hwang JN. Neural networks for intelligent multimedia processing. Proc IEEE 1998; 86: 1244–72CrossRef Kung SY, Hwang JN. Neural networks for intelligent multimedia processing. Proc IEEE 1998; 86: 1244–72CrossRef
42.
go back to reference Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon Press, 1995 Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon Press, 1995
43.
go back to reference Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning. New York: Springer-Verlag, 2001 Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning. New York: Springer-Verlag, 2001
44.
go back to reference Hirschhorn JN, Daly MI. Genome-wide association studies for common diseases and complex traits. Nature Review Genet 2005; 6: 95–108CrossRef Hirschhorn JN, Daly MI. Genome-wide association studies for common diseases and complex traits. Nature Review Genet 2005; 6: 95–108CrossRef
45.
go back to reference North BV, Curtis D, Cassell PG, et al. Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes. Ann Hum Genet 2003; 67: 348–56PubMedCrossRef North BV, Curtis D, Cassell PG, et al. Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes. Ann Hum Genet 2003; 67: 348–56PubMedCrossRef
46.
go back to reference Horstmann S, Lucae S, Menke A, et al. Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment. Neuropsychopharmacology 2010; 35: 727–40PubMedCrossRef Horstmann S, Lucae S, Menke A, et al. Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment. Neuropsychopharmacology 2010; 35: 727–40PubMedCrossRef
47.
go back to reference Hsiao TJ, Wu LS, Huang SY, et al. Effect of the common G-866A 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: 101–6PubMedCrossRef Hsiao TJ, Wu LS, Huang SY, et al. Effect of the common G-866A 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: 101–6PubMedCrossRef
48.
go back to reference Corvol H, De Giacomo A, Eng C, et al. Genetic ancestry modifies pharmacogenetic gene-gene interaction for asthma. Pharmacogenet Genomics 2009; 19: 489–96PubMedCrossRef Corvol H, De Giacomo A, Eng C, et al. Genetic ancestry modifies pharmacogenetic gene-gene interaction for asthma. Pharmacogenet Genomics 2009; 19: 489–96PubMedCrossRef
49.
go back to reference Choudhry S, Que LG, Yang Z, et al. GSNO reductase and beta2-adrenergic receptor gene-gene interaction: bronchodilator responsiveness to albuterol. Pharmacogenet Genomics 2010; 20: 351–8PubMedCrossRef Choudhry S, Que LG, Yang Z, et al. GSNO reductase and beta2-adrenergic receptor gene-gene interaction: bronchodilator responsiveness to albuterol. Pharmacogenet Genomics 2010; 20: 351–8PubMedCrossRef
50.
go back to reference Sharma S, Das M, Kumar A, et al. Interaction of genes from influxmetabolism-efflux pathway and their influence on methotrexate efficacy in rheumatoid arthritis patients among Indians. Pharmacogenet Genomics 2008; 18: 1041–9PubMedCrossRef Sharma S, Das M, Kumar A, et al. Interaction of genes from influxmetabolism-efflux pathway and their influence on methotrexate efficacy in rheumatoid arthritis patients among Indians. Pharmacogenet Genomics 2008; 18: 1041–9PubMedCrossRef
51.
go back to reference Liou YJ, Bai YM, Lin E, et al. Gene-gene interactions of the INSIG1 and INSIG2 in metabolic syndrome in schizophrenic patients treated with atypical antipsychotics. Pharmacogenomics J 2012; 12: 54–61PubMedCrossRef Liou YJ, Bai YM, Lin E, et al. Gene-gene interactions of the INSIG1 and INSIG2 in metabolic syndrome in schizophrenic patients treated with atypical antipsychotics. Pharmacogenomics J 2012; 12: 54–61PubMedCrossRef
52.
go back to reference Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 2003; 289: 3095–105PubMedCrossRef Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 2003; 289: 3095–105PubMedCrossRef
53.
go back to reference Hasin DS, Goodwin RD, Stinson FS, et al. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 2005; 62: 1097–106PubMedCrossRef Hasin DS, Goodwin RD, Stinson FS, et al. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 2005; 62: 1097–106PubMedCrossRef
54.
go back to reference aan het Rot M, Mathew SJ, Charney DS. Neurobiological mechanisms in major depressive disorder. CMAJ 2009; 180: 305–13CrossRef aan het Rot M, Mathew SJ, Charney DS. Neurobiological mechanisms in major depressive disorder. CMAJ 2009; 180: 305–13CrossRef
55.
go back to reference Lin E, Chen PS, Lee IH, et al. Modeling short-term antidepressant responsiveness with artificial neural networks. Open Access Bioinformatics 2010; 2: 55–60CrossRef Lin E, Chen PS, Lee IH, et al. Modeling short-term antidepressant responsiveness with artificial neural networks. Open Access Bioinformatics 2010; 2: 55–60CrossRef
56.
go back to reference Lin E, Hwang Y, Tzeng CM. A case study of the utility of the HapMap database for pharmacogenomic haplotype analysis in the Taiwanese population. Mol Diagn Ther 2006; 10: 367–70 Lin E, Hwang Y, Tzeng CM. A case study of the utility of the HapMap database for pharmacogenomic haplotype analysis in the Taiwanese population. Mol Diagn Ther 2006; 10: 367–70
57.
go back to reference Lekman M, Paddock S, McMahon FJ. Pharmacogenetics of major depression: insights from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. Mol Diagn Ther 2008; 12: 321–30PubMedCrossRef Lekman M, Paddock S, McMahon FJ. Pharmacogenetics of major depression: insights from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. Mol Diagn Ther 2008; 12: 321–30PubMedCrossRef
58.
59.
go back to reference Lin E, Chen PS. Pharmacogenomics with antidepressants in the STAR*D study. Pharmacogenomics 2008; 9: 935–46PubMedCrossRef Lin E, Chen PS. Pharmacogenomics with antidepressants in the STAR*D study. Pharmacogenomics 2008; 9: 935–46PubMedCrossRef
60.
go back to reference Lin E, Hsu SY. Gender differences and pharmacogenomics with antidepressants in depression. In: Hernandez P, Alonso S, editors. Women and depression. New York: Nova Science Publishers, 2009 Lin E, Hsu SY. Gender differences and pharmacogenomics with antidepressants in depression. In: Hernandez P, Alonso S, editors. Women and depression. New York: Nova Science Publishers, 2009
61.
go back to reference Lin E, Chen PS, Huang LC, et al. Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders. Pharmacogenomics Personalized Med 2008; 1: 1–6CrossRef Lin E, Chen PS, Huang LC, et al. Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders. Pharmacogenomics Personalized Med 2008; 1: 1–6CrossRef
62.
go back to reference Horstmann S, Binder EB. Pharmacogenomics of antidepressant drugs. Pharmacol Ther 2009; 124: 57–73PubMedCrossRef Horstmann S, Binder EB. Pharmacogenomics of antidepressant drugs. Pharmacol Ther 2009; 124: 57–73PubMedCrossRef
64.
go back to reference Siffert W. G protein polymorphisms in hypertension, atherosclerosis, and diabetes. Annu Rev Med 2005; 56: 17–28PubMedCrossRef Siffert W. G protein polymorphisms in hypertension, atherosclerosis, and diabetes. Annu Rev Med 2005; 56: 17–28PubMedCrossRef
66.
go back to reference Norton N, Owen MJ. HTR2A: association and expression studies in neuropsychiatric genetics. Ann Med 2005; 37: 121–9PubMedCrossRef Norton N, Owen MJ. HTR2A: association and expression studies in neuropsychiatric genetics. Ann Med 2005; 37: 121–9PubMedCrossRef
67.
go back to reference Serretti A, Drago A, De Ronchi D. HTR2A gene variants and psychiatric disorders: a review of current literature and selection of SNPs for future studies. Curr Med Chem 2007; 14: 2053–69PubMedCrossRef Serretti A, Drago A, De Ronchi D. HTR2A gene variants and psychiatric disorders: a review of current literature and selection of SNPs for future studies. Curr Med Chem 2007; 14: 2053–69PubMedCrossRef
68.
go back to reference McMahon FJ, Buervenich S, Charney D, et al. Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet 2006; 78: 804–14PubMedCrossRef McMahon FJ, Buervenich S, Charney D, et al. Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet 2006; 78: 804–14PubMedCrossRef
69.
go back to reference Parsons MJ, D’Souza UM, Arranz MJ, et al. The -1438A/G polymorphism in the 5-hydroxytryptamine type 2A receptor gene affects promoter activity. Biol Psychiatry 2004; 56: 406–10PubMedCrossRef Parsons MJ, D’Souza UM, Arranz MJ, et al. The -1438A/G polymorphism in the 5-hydroxytryptamine type 2A receptor gene affects promoter activity. Biol Psychiatry 2004; 56: 406–10PubMedCrossRef
70.
go back to reference Caspi A, Sugden K, Moffitt TE, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science 2003; 301: 386–9PubMedCrossRef Caspi A, Sugden K, Moffitt TE, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science 2003; 301: 386–9PubMedCrossRef
71.
go back to reference Sharma B, Henderson DC. Sibutramine: current status as an anti-obesity drug and its future perspectives. Expert Opin Pharmacother 2008; 9: 2161–73PubMedCrossRef Sharma B, Henderson DC. Sibutramine: current status as an anti-obesity drug and its future perspectives. Expert Opin Pharmacother 2008; 9: 2161–73PubMedCrossRef
72.
go back to reference Tziomalos K, Krassas GE, Tzotzas T. The use of sibutramine in the management of obesity and related disorders: an update. Vasc Health Risk Manag 2009; 5: 441–52PubMed Tziomalos K, Krassas GE, Tzotzas T. The use of sibutramine in the management of obesity and related disorders: an update. Vasc Health Risk Manag 2009; 5: 441–52PubMed
73.
go back to reference Grudell AB, Sweetser S, Camilleri M, et al. A controlled pharmacogenetic trial of sibutramine on weight loss and body composition in obese or overweight adults. Gastroenterology 2008; 135: 1142–54PubMedCrossRef Grudell AB, Sweetser S, Camilleri M, et al. A controlled pharmacogenetic trial of sibutramine on weight loss and body composition in obese or overweight adults. Gastroenterology 2008; 135: 1142–54PubMedCrossRef
74.
go back to reference Hauner H, Meier M, Jöckel KH, et al. Prediction of successful weight reduction under sibutramine therapy through genotyping of the G-protein beta3 subunit gene (GNB3) C825T polymorphism. Pharmacogenetics 2003; 13: 453–9PubMedCrossRef Hauner H, Meier M, Jöckel KH, et al. Prediction of successful weight reduction under sibutramine therapy through genotyping of the G-protein beta3 subunit gene (GNB3) C825T polymorphism. Pharmacogenetics 2003; 13: 453–9PubMedCrossRef
75.
go back to reference Hsiao DJ, Wu LS, Huang SY, et al. Weight loss and body fat reduction under sibutramine therapy in obesity with the C825T polymorphism in the guanine nucleotide binding protein beta polypeptide 3 gene. Pharmacogenet Genomics 2009; 19: 730–3PubMedCrossRef Hsiao DJ, Wu LS, Huang SY, et al. Weight loss and body fat reduction under sibutramine therapy in obesity with the C825T polymorphism in the guanine nucleotide binding protein beta polypeptide 3 gene. Pharmacogenet Genomics 2009; 19: 730–3PubMedCrossRef
76.
go back to reference Vazquez Roque MI, Camilleri M, Clark MM, et al. Alteration of gastric functions and candidate genes associated with weight reduction in response to sibutramine. Clin Gastroenterol Hepatol 2007; 5: 829–37PubMedCrossRef Vazquez Roque MI, Camilleri M, Clark MM, et al. Alteration of gastric functions and candidate genes associated with weight reduction in response to sibutramine. Clin Gastroenterol Hepatol 2007; 5: 829–37PubMedCrossRef
77.
go back to reference Hsiao TJ, Wu LS, Huang SY, et al. A common variant in the adiponectin gene on weight loss and body composition under sibutramine therapy in obesity. Clin Pharmacol Adv Applic 2010; 2: 105–10 Hsiao TJ, Wu LS, Huang SY, et al. A common variant in the adiponectin gene on weight loss and body composition under sibutramine therapy in obesity. Clin Pharmacol Adv Applic 2010; 2: 105–10
78.
go back to reference Lane HY, Tsai GE, Lin E. Research highlights from the latest articles in 5-HTTLPR pharmacogenomics. Personalized Med 2010; 7: 139–41CrossRef Lane HY, Tsai GE, Lin E. Research highlights from the latest articles in 5-HTTLPR pharmacogenomics. Personalized Med 2010; 7: 139–41CrossRef
79.
go back to reference Lin E, Chen PS. Molecular genetics of human personality traits for psychiatric, behavioral, and substance-related disorders. Open Translat Med J 2009; 1: 1–8CrossRef Lin E, Chen PS. Molecular genetics of human personality traits for psychiatric, behavioral, and substance-related disorders. Open Translat Med J 2009; 1: 1–8CrossRef
80.
82.
go back to reference Hwang Y, Chen EY, Gu ZJ, et al. Genetic predisposition of responsiveness to therapy for chronic hepatitis C. Pharmacogenomics 2006; 7: 697–709PubMedCrossRef Hwang Y, Chen EY, Gu ZJ, et al. Genetic predisposition of responsiveness to therapy for chronic hepatitis C. Pharmacogenomics 2006; 7: 697–709PubMedCrossRef
83.
go back to reference Hijikata M, Ohta Y, Mishiro S. Identification of a single nucleotide polymorphism in the MxA gene promoter (G/T at nt-88) correlated with the response of hepatitis C patients to interferon. Intervirology 2000; 43: 124–7PubMedCrossRef Hijikata M, Ohta Y, Mishiro S. Identification of a single nucleotide polymorphism in the MxA gene promoter (G/T at nt-88) correlated with the response of hepatitis C patients to interferon. Intervirology 2000; 43: 124–7PubMedCrossRef
84.
go back to reference Yee LJ, Tang J, Gibson AW, et al. Interleukin 10 polymorphisms as predictors of sustained response in antiviral therapy for chronic hepatitis C infection. Hepatology 2001; 33: 708–12PubMedCrossRef Yee LJ, Tang J, Gibson AW, et al. Interleukin 10 polymorphisms as predictors of sustained response in antiviral therapy for chronic hepatitis C infection. Hepatology 2001; 33: 708–12PubMedCrossRef
85.
go back to reference Sugimoto Y, Kuzushita N, Takehara T, et al. A single nucleotide polymorphism of the low molecular mass polypeptide 7 gene influences the interferon response in patients with chronic hepatitis C. J Viral Hepat 2002; 9: 377–84PubMedCrossRef Sugimoto Y, Kuzushita N, Takehara T, et al. A single nucleotide polymorphism of the low molecular mass polypeptide 7 gene influences the interferon response in patients with chronic hepatitis C. J Viral Hepat 2002; 9: 377–84PubMedCrossRef
86.
go back to reference 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: 39–44PubMed 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: 39–44PubMed
87.
go back to reference Kitamura A, Takahashi K, Okajima A, et al. Induction of the human gene for p44, a hepatitis C-associated microtubular aggregate protein, by interferon-α/gb. Eur J Biochem 1994; 224: 877–83PubMedCrossRef Kitamura A, Takahashi K, Okajima A, et al. Induction of the human gene for p44, a hepatitis C-associated microtubular aggregate protein, by interferon-α/gb. Eur J Biochem 1994; 224: 877–83PubMedCrossRef
88.
go back to reference Hallen LC, Burki Y, Ebeling M, et al. Antiproliferative activity of the human IFN-alpha-inducible protein IFI44. J Interferon Cytokine Res 2007; 27: 675–80PubMedCrossRef Hallen LC, Burki Y, Ebeling M, et al. Antiproliferative activity of the human IFN-alpha-inducible protein IFI44. J Interferon Cytokine Res 2007; 27: 675–80PubMedCrossRef
89.
go back to reference Martinon F, Tschopp J. Inflammatory caspases and inflammasomes: master switches of inflammation. Cell Death Differ 2007; 14: 10–22PubMedCrossRef Martinon F, Tschopp J. Inflammatory caspases and inflammasomes: master switches of inflammation. Cell Death Differ 2007; 14: 10–22PubMedCrossRef
90.
go back to reference Lin XY, Choi MS, Porter AG. Expression analysis of the human caspase-1 subfamily reveals specific regulation of the CASP5 gene by lipopolysaccharide and interferon-gamma. J Biol Chem 2000; 275: 39920–6PubMedCrossRef Lin XY, Choi MS, Porter AG. Expression analysis of the human caspase-1 subfamily reveals specific regulation of the CASP5 gene by lipopolysaccharide and interferon-gamma. J Biol Chem 2000; 275: 39920–6PubMedCrossRef
91.
go back to reference Dong LM, Brennan P, Karami S, et al. An analysis of growth, differentiation and apoptosis genes with risk of renal cancer. PLoS One 2009; 4: e4895PubMedCrossRef Dong LM, Brennan P, Karami S, et al. An analysis of growth, differentiation and apoptosis genes with risk of renal cancer. PLoS One 2009; 4: e4895PubMedCrossRef
92.
go back to reference Ulybina YM, Kuligina ESh, Mitiushkina NV, et al. Coding polymorphisms in Casp5, Casp8 and DR4 genes may play a role in predisposition to lung cancer. Cancer Lett 2009; 278: 183–91PubMedCrossRef Ulybina YM, Kuligina ESh, Mitiushkina NV, et al. Coding polymorphisms in Casp5, Casp8 and DR4 genes may play a role in predisposition to lung cancer. Cancer Lett 2009; 278: 183–91PubMedCrossRef
93.
go back to reference Quaye L, Dafou D, Ramus SJ, et al. Functional complementation studies identify candidate genes and common genetic variants associated with ovarian cancer survival. Hum Mol Genet 2009; 18: 1869–78PubMedCrossRef Quaye L, Dafou D, Ramus SJ, et al. Functional complementation studies identify candidate genes and common genetic variants associated with ovarian cancer survival. Hum Mol Genet 2009; 18: 1869–78PubMedCrossRef
94.
go back to reference Ulybina YM, Kuligina ESh, Mitiushkina NV, et al. Evidence for depletion of CASP5 Ala90Thr heterozygous genotype in aged subjects. Exp Gerontol 2010; 45: 726–9PubMedCrossRef Ulybina YM, Kuligina ESh, Mitiushkina NV, et al. Evidence for depletion of CASP5 Ala90Thr heterozygous genotype in aged subjects. Exp Gerontol 2010; 45: 726–9PubMedCrossRef
95.
go back to reference Soranzo N, Rendon A, Gieger C, et al. A novel variant on chromosome 7q22.3 associated with mean platelet volume, counts, and function. Blood 2009; 113: 3831–7PubMedCrossRef Soranzo N, Rendon A, Gieger C, et al. A novel variant on chromosome 7q22.3 associated with mean platelet volume, counts, and function. Blood 2009; 113: 3831–7PubMedCrossRef
96.
go back to reference Johnson AD, Yanek LR, Chen MH, et al. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat Genet 2010; 42: 608–13PubMedCrossRef Johnson AD, Yanek LR, Chen MH, et al. Genome-wide meta-analyses identifies seven loci associated with platelet aggregation in response to agonists. Nat Genet 2010; 42: 608–13PubMedCrossRef
97.
go back to reference Lin E, Tsai SJ. Gene-gene interactions in a context of individual variability in antipsychotic drug pharmacogenomics. Curr Pharmacogenomics Personalized Med 2011; 9: 323–31CrossRef Lin E, Tsai SJ. Gene-gene interactions in a context of individual variability in antipsychotic drug pharmacogenomics. Curr Pharmacogenomics Personalized Med 2011; 9: 323–31CrossRef
98.
99.
go back to reference Rosenberg NA, Huang L, Jewett EM, et al. Genome-wide association studies in diverse populations. Nat Rev Genet 2010; 11: 356–66PubMedCrossRef Rosenberg NA, Huang L, Jewett EM, et al. Genome-wide association studies in diverse populations. Nat Rev Genet 2010; 11: 356–66PubMedCrossRef
100.
go back to reference Elbers CC, van Eijk KR, Franke L, et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 2009; 33: 419–31PubMedCrossRef Elbers CC, van Eijk KR, Franke L, et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 2009; 33: 419–31PubMedCrossRef
101.
go back to reference Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 2010; 86: 6–22PubMedCrossRef Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 2010; 86: 6–22PubMedCrossRef
102.
go back to reference Gayán J, González-Pérez A, Bermudo F, et al. A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis. BMC Genomics 2008; 9: 360PubMedCrossRef Gayán J, González-Pérez A, Bermudo F, et al. A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis. BMC Genomics 2008; 9: 360PubMedCrossRef
103.
go back to reference Jiang R, Tang W, Wu X, et al. A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics 2009; 10: S65PubMedCrossRef Jiang R, Tang W, Wu X, et al. A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics 2009; 10: S65PubMedCrossRef
104.
go back to reference Tang W, Wu X, Jiang R, et al. Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet 2009; 5: e1000464PubMedCrossRef Tang W, Wu X, Jiang R, et al. Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet 2009; 5: e1000464PubMedCrossRef
105.
go back to reference Wongseree W, Assawamakin A, Piroonratana T, et al. Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses. BMC Bioinformatics 2009; 10: 294PubMedCrossRef Wongseree W, Assawamakin A, Piroonratana T, et al. Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses. BMC Bioinformatics 2009; 10: 294PubMedCrossRef
106.
go back to reference Emily M, Mailund T, Hein J, et al. Using biological networks to search for interacting loci in genome-wide association studies. Eur J Hum Genet 2009; 17: 1231–40PubMedCrossRef Emily M, Mailund T, Hein J, et al. Using biological networks to search for interacting loci in genome-wide association studies. Eur J Hum Genet 2009; 17: 1231–40PubMedCrossRef
107.
go back to reference Yang HC, Liang YJ, Wu YL, et al. Genome-wide association study of young-onset hypertension in the Han Chinese population of Taiwan. PLoS One 2009; 4: e5459PubMedCrossRef Yang HC, Liang YJ, Wu YL, et al. Genome-wide association study of young-onset hypertension in the Han Chinese population of Taiwan. PLoS One 2009; 4: e5459PubMedCrossRef
108.
go back to reference Wan X, Yang C, Yang Q, et al. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics 2010; 26: 30–7PubMedCrossRef Wan X, Yang C, Yang Q, et al. Predictive rule inference for epistatic interaction detection in genome-wide association studies. Bioinformatics 2010; 26: 30–7PubMedCrossRef
109.
go back to reference Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 2010; 11: 415–25PubMedCrossRef Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 2010; 11: 415–25PubMedCrossRef
110.
go back to reference Gorlov IP, Gorlova OY, Sunyaev SR, et al. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am J Hum Genet 2008; 82: 100–12PubMedCrossRef Gorlov IP, Gorlova OY, Sunyaev SR, et al. Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am J Hum Genet 2008; 82: 100–12PubMedCrossRef
111.
go back to reference Morris AP, Zeggini E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 2010; 34: 188–93PubMedCrossRef Morris AP, Zeggini E. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 2010; 34: 188–93PubMedCrossRef
112.
go back to reference Liu DJ, Leal SM. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 2010; 6: e1001156PubMedCrossRef Liu DJ, Leal SM. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 2010; 6: e1001156PubMedCrossRef
113.
go back to reference Wang CH, Hwang Y, Lin E. Pharmacogenomics of chronic hepatitis C therapy with genome-wide association studies. J Exper Pharmacol 2010; 2: 73–82CrossRef Wang CH, Hwang Y, Lin E. Pharmacogenomics of chronic hepatitis C therapy with genome-wide association studies. J Exper Pharmacol 2010; 2: 73–82CrossRef
114.
go back to reference Ge D, Fellay J, Thompson AJ, et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 2009; 461: 399–401PubMedCrossRef Ge D, Fellay J, Thompson AJ, et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 2009; 461: 399–401PubMedCrossRef
115.
go back to reference Suppiah V, Moldovan M, Ahlenstiel G, et al. IL28B is associated with response to chronic hepatitis C interferon-alpha and ribavirin therapy. Nat Genet 2009; 41: 1100–4PubMedCrossRef Suppiah V, Moldovan M, Ahlenstiel G, et al. IL28B is associated with response to chronic hepatitis C interferon-alpha and ribavirin therapy. Nat Genet 2009; 41: 1100–4PubMedCrossRef
116.
go back to reference Tanaka Y, Nishida N, Sugiyama M, et al. Genome-wide association of IL28B with response to pegylated interferon-a and ribavirin therapy for chronic hepatitis C. Nat Genet 2009; 10: 1105–11CrossRef Tanaka Y, Nishida N, Sugiyama M, et al. Genome-wide association of IL28B with response to pegylated interferon-a and ribavirin therapy for chronic hepatitis C. Nat Genet 2009; 10: 1105–11CrossRef
117.
go back to reference Rauch A, Kutalik Z, Descombes P, et al. Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study. Gastroenterology 2010; 138: 1338–45PubMedCrossRef Rauch A, Kutalik Z, Descombes P, et al. Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study. Gastroenterology 2010; 138: 1338–45PubMedCrossRef
118.
go back to reference Fox BA, Sheppard PO, O’Hara PJ. The role of genomic data in the discovery, annotation and evolutionary interpretation of the interferon-lambda family. PLoS One 2009; 4: e4933PubMedCrossRef Fox BA, Sheppard PO, O’Hara PJ. The role of genomic data in the discovery, annotation and evolutionary interpretation of the interferon-lambda family. PLoS One 2009; 4: e4933PubMedCrossRef
119.
go back to reference Dellgren C, Gad HH, Hamming OJ, et al. Human interferon-lambda3 is a potent member of the type III interferon family. Genes Immun 2009; 10: 125–31PubMedCrossRef Dellgren C, Gad HH, Hamming OJ, et al. Human interferon-lambda3 is a potent member of the type III interferon family. Genes Immun 2009; 10: 125–31PubMedCrossRef
120.
go back to reference 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: 219–23PubMedCrossRef 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: 219–23PubMedCrossRef
121.
go back to reference Ising M, Lucae S, Binder EB, et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry 2009; 66: 966–75PubMedCrossRef Ising M, Lucae S, Binder EB, et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry 2009; 66: 966–75PubMedCrossRef
122.
go back to reference Garriock HA, Kraft JB, Shyn SI, et al. A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry 2010; 67: 133–8PubMedCrossRef Garriock HA, Kraft JB, Shyn SI, et al. A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry 2010; 67: 133–8PubMedCrossRef
123.
go back to reference Uher R, Perroud N, Ng MY, et al. Genome-wide pharmacogenetics of anti-depressant response in the GENDEP project. Am J Psychiatry 2010; 167: 555–64PubMedCrossRef Uher R, Perroud N, Ng MY, et al. Genome-wide pharmacogenetics of anti-depressant response in the GENDEP project. Am J Psychiatry 2010; 167: 555–64PubMedCrossRef
124.
go back to reference Laje G, McMahon FJ. Genome-wide association studies of antidepressant outcome: a brief review. Prog Neuropsychopharmacol Biol Psychiatry 2011; 35: 1553–7PubMedCrossRef Laje G, McMahon FJ. Genome-wide association studies of antidepressant outcome: a brief review. Prog Neuropsychopharmacol Biol Psychiatry 2011; 35: 1553–7PubMedCrossRef
125.
go back to reference Malhotra AK. The pharmacogenetics of depression: enter the GWAS. Am J Psychiatry 2010; 167: 493–5PubMedCrossRef Malhotra AK. The pharmacogenetics of depression: enter the GWAS. Am J Psychiatry 2010; 167: 493–5PubMedCrossRef
126.
go back to reference Amos W, Driscoll E, Hoffman JI. Candidate genes versus genome-wide associations: which are better for detecting genetic susceptibility to infectious disease? Proc Biol Sci 2011; 278: 1183–8PubMedCrossRef Amos W, Driscoll E, Hoffman JI. Candidate genes versus genome-wide associations: which are better for detecting genetic susceptibility to infectious disease? Proc Biol Sci 2011; 278: 1183–8PubMedCrossRef
127.
go back to reference Uher R, Huezo-Diaz P, Perroud N, et al. Genetic predictors of response to antidepressants in the GENDEP project. Pharmacogenomics J 2009; 9: 225–33PubMedCrossRef Uher R, Huezo-Diaz P, Perroud N, et al. Genetic predictors of response to antidepressants in the GENDEP project. Pharmacogenomics J 2009; 9: 225–33PubMedCrossRef
128.
go back to reference Lucena MI, Molokhia M, Shen Y, et al. Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology 2011; 141: 338–47PubMedCrossRef Lucena MI, Molokhia M, Shen Y, et al. Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology 2011; 141: 338–47PubMedCrossRef
129.
go back to reference 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: 81PubMedCrossRef 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: 81PubMedCrossRef
130.
go back to reference Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007; 23: 2507–17PubMedCrossRef Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007; 23: 2507–17PubMedCrossRef
131.
go back to reference Hsieh CH, Hung YJ, Pei D, et al. Pilot association study between a common variant in the non-muscle myosin heavy chain 9 (MYH9) gene and diabetic renal disease in a Taiwanese population with type 2 diabetes. Appl Clin Genet 2010; 3: 17–22 Hsieh CH, Hung YJ, Pei D, et al. Pilot association study between a common variant in the non-muscle myosin heavy chain 9 (MYH9) gene and diabetic renal disease in a Taiwanese population with type 2 diabetes. Appl Clin Genet 2010; 3: 17–22
Metadata
Title
Assessing Gene-Gene Interactions in Pharmacogenomics
Authors
Hsien-Yuan Lane
Guochuan E. Tsai
Dr Eugene Lin
Publication date
01-02-2012
Publisher
Springer International Publishing
Published in
Molecular Diagnosis & Therapy / Issue 1/2012
Print ISSN: 1177-1062
Electronic ISSN: 1179-2000
DOI
https://doi.org/10.1007/BF03256426

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