Association of the rs6235 variant in the proprotein convertase subtilisin/kexin type 1 (PCSK1) gene with obesity and related traits in a Taiwanese population.

One particularly interesting single nucleotide polymorphism (SNP), rs6235 (encoding an S690T substitution), in the proprotein convertase subtilisin/kexin type 1 (PCSK1) gene has been widely associated with obesity in several European cohorts. The present study was intended to investigate the association between the PCSK1 rs6235 SNP and the prevalence of overweight or obesity, or obesity-related metabolic traits in a Taiwanese population. A total of 964 Taiwanese subjects with general health examinations were analyzed. Our data revealed no association of PCSK1 rs6235 with the risk of obesity or overweight in the complete subjects. However, the PCSK1 rs6235 SNP exhibited a significant association with overweight among the male subjects (P=0.03), but not among the female subjects. Furthermore, the carriers of GG variant had a significantly higher waist circumference than those with the CC variant (82.5 ± 11.5 vs. 81.2 ± 10.2 cm; P=0.01) and those with the CG variant (82.5 ± 11.5 vs. 81.4 ± 10.4 cm; P=0.021). In addition, the carriers of GG variant had a higher diastolic blood pressure than those with the CC variant (81.9 ± 14.2 vs. 80.3 ± 12.9 mm Hg; P=0.023). Our study indicates that the PCSK1 rs6235 SNP may contribute to the risk of overweight in men and predict obesity-related metabolic traits such as waist circumference and diastolic blood pressure in Taiwanese subjects.

Association of the C825T polymorphism in the GNB3 gene with obesity and metabolic phenotypes in a Taiwanese population.

The relationship between obesity and a single nucleotide polymorphism (SNP), rs5443 (C825T), in the guanine nucleotide binding protein beta polypeptide 3 (GNB3) gene is currently inconsistent. In this study, we aimed to reassess whether the GNB3 rs5443 SNP could influence obesity and obesity-related metabolic traits in a Taiwanese population. A total of 983 Taiwanese subjects with general health examinations were genotyped. Based on the criteria defined by the Department of Health in Taiwan, the terms "overweight" and "obesity" are defined as 24 ≦ BMI < 27 and BMI ≧ 27, respectively. Compared to the carrier of the combined CT + TT genotypes of the GNB3 rs5443 polymorphism, triglyceride was significantly higher for the carrier of CC genotype in the complete sample population (128.2 ± 93.2 vs. 114.3 ± 79.1 mg/dl; P = 0.041). In addition, the carriers of CC variant had a higher total cholesterol than those with the combined CT + TT variants (194.5 ± 36.8 vs. 187.9 ± 33.0 mg/dl; P = 0.019) in the complete sample population. In the normal controls, both triglyceride (P = 0.018) and total cholesterol (P = 0.011) were also significantly higher in the CC homozygotes than in the combined CT + TT genotypes. However, the GNB3 rs5443 SNP did not exhibit any significant association with obesity or overweight among the subjects. Our study indicates that the CC genotype of the GNB3 rs5443 SNP may predict higher obesity-related metabolic traits such as triglyceride and total cholesterol in non-obese Taiwanese subjects (but not in obese subjects).

A base-calling algorithm for Tm-shifted melting curve SNP assay.

BACKGROUND: Tm-shifted melting curve SNP assays are a class of homogeneous, low-cost genotyping assays. Alleles manifest themselves as signal peaks in the neighbourhood of theoretical allele-specific melting temperatures. Base calling for these assays has mostly relied on unsupervised algorithm or human visual inspection to date. However, a practical clinical test needs to handle one or few individual samples at a time. This could pose a challenge for unsupervised algorithms which usually require a large number of samples to define alleles-representing signal clusters on the fly. METHODS: We presented a supervised base-calling algorithm and software for Tm-shifted melting curve SNP assays. The algorithm comprises a peak detection procedure and an ordinal regression model. The peak detection procedure is required for building models as well as handling new samples. Ordinal regression is proposed because signal intensities of alleles AA, AB, and BB usually follow an ordinal pattern with the heterozygous allele lie between two distinct homozygous alleles. Coefficients of the ordinal regression model are first trained and then used for base calling. RESULTS: A dataset of 12 SNPs of 44 unrelated persons was used for a demonstration purpose. The call rate is 99.6%. Among the base calls, 99.1% are identical to those made by the sequencing method. A small fraction of the melting curve signals (0.4%) is declared as "no call" for further human inspection. A software was implemented using the Java language, providing a graphical user interface for the visualization and handling of multiple melting curve signals. CONCLUSIONS: Tm-shifted melting curve SNP assays, together with the proposed base calling algorithm and software, provide a practical solution for genetic tests on a clinical setting. The software is available in http://www.bioinformatics.org/mcsnp/wiki/Main/HomePage.

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.

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.

Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms.

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.

Pharmacogenomics of chronic hepatitis C therapy with genome-wide association studies.

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.

A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy.

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.

Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C.

INTRODUCTION: In studies of pharmacogenomics, it is essential to address gene-gene and gene-environment interactions to describe complex traits involving pharmacokinetic and pharmacodynamic mechanisms. In this work, our goal is to detect gene-gene and gene-environment interactions resulting from an analysis of chronic hepatitis C patients' clinical factors including SNPs, viral genotype, viral load, age and gender. MATERIALS & METHODS: We collected blood samples from 523 chronic hepatitis C patients who had received interferon and ribavirin combination therapy. Based on the treatment strategy for chronic hepatitis C patients, we focused our search for candidate genes involved in pathways related to interferon signaling and immunomodulation. To investigate gene-gene and gene-environment interactions, we implemented an artificial neural network-based method for identifying significant interactions between clinical factors with the fivefold crossvalidation method and permutation tests. The artificial neural network model was trained by an algorithm with an adaptive momentum and learning rate. RESULTS: A total of 20 SNPs were selected from six candidate genes including adenosine deaminase-RNA-specific (ADAR), caspase 5 (CASP5), interferon consensus sequence binding protein 1 (ICSBP1), interferon-induced protein 44 (IFI44), phosphoinositide-3-kinase catalytic gamma polypeptide (PIK3CG), and transporter 2 ATP-binding cassette subfamily B (TAP2) genes. By applying our artificial neural network-based approach, IFI44 was found in the significant two-locus, three-locus and four-locus gene-gene effect models, as well as in the significant two-factor and three-factor gene-environment effect models. Furthermore, viral genotype remained in the best two-factor, three-factor and four-factor gene-environment models. These results support the hypothesis that IFI44 and viral genotype may play a role in the pharmacogenomics of interferon treatment. In addition, our approach identified a panel of ten clinical factors that may be more significant than the others for further study. CONCLUSION: We demonstrated that our artificial neural network-based approach is a promising method to assess the gene-gene and gene-environment interactions for interferon and ribavirin combination treatment in chronic hepatitis C patients by using clinical factors such as SNPs, viral genotype, viral load, age and gender.

Pattern-recognition techniques with haplotype analysis in pharmacogenomics.

Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. However, it would be extremely inefficient to test all the 10 million common SNPs for an association study. Here we review haplotype analysis and pattern-recognition techniques to systematically select candidate SNPs for candidate-gene association studies in pharmacogenomics. First, we survey linkage disequilibrium methods to identify tag SNPs and explore the use of haplotypes as genetic markers that are correlated and associated with drug efficacy. Secondly, we investigate pattern-recognition algorithms and statistical analyses to assess drug efficacy based on SNPs and other factors. Finally, we study pattern-recognition approaches to evaluate the epistasis among genes and SNPs. These techniques may provide tools for clinical association studies and help find genes/SNPs involved in responses to therapeutic drugs or adverse drug reactions.

A case study of the utility of the HapMap database for pharmacogenomic haplotype analysis in the Taiwanese population.

BACKGROUND: Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. The goal of this work was to evaluate the feasibility of using SNP information of the Han Chinese in Beijing (CHB) population from the HapMap database for clinical association studies in the Taiwanese (TWN) population. METHODS: We compared the HapMap populations with our TWN study population with regard to allele frequencies for common SNPs in two candidate genes for antidepressant treatment response to determine the applicability of the HapMap CHB data for SNP selection in the TWN population. RESULTS AND CONCLUSION: Our preliminary results suggest that there was no significant difference, in terms of allele and haplotype frequencies, between the CHB population of the HapMap database and the TWN population collected by Vita Genomics Inc. Therefore, it is possible to use the CHB population of the HapMap database for SNP selection in association studies for the TWN population. Using haplotype analysis, we generated a panel of SNPs that may be strongly relevant to antidepressant response in this population.

An artificial neural network approach to the drug efficacy of interferon treatments.

INTRODUCTION: Interferon taken alone or in combination with ribavirin can be used for the treatment of persons with chronic hepatitis C. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the treatments. In this work, our goal is to develop a prediction model resulting from the analysis of chronic hepatitis C patients' single nucleotide polymorphisms, viral genotype, viral load, age and gender, to predict the responsiveness of interferon combination treatment. MATERIALS AND METHODS: We collected blood samples from 523 chronic hepatitis C patients that had received interferon and ribavirin combination therapy. Based on the current treatment strategy for chronic hepatitis C patients, we focused our search for candidate genes involved in pathways related to interferon signaling and immunomodulation. With artificial neural network algorithms, we then developed pattern recognition methodologies to achieve predictions among the patients. The artificial neural network model was trained by an algorithm with an adaptive momentum and learning rate. RESULTS: There were seven single nucleotide polymorphisms selected from six candidate genes including adenosine deaminase-RNA-specific, caspase 5, interferon consensus sequence binding protein 1, interferon-induced protein 44, phosphoinositide-3-kinase catalytic gamma polypeptide and transporter 2 ATP-binding cassette subfamily B genes. We further applied the artificial neural network algorithms with these seven single nucleotide polymorphisms, viral genotype, viral load, age and gender information to build tools for predicting the responsiveness of interferon. Based on the fivefold cross-validation method to evaluate the performance, the model achieved a high success rate of prediction. CONCLUSION: We demonstrated that a trained artificial neural network model is a promising method for providing the inference from factors such as single nucleotide polymorphisms, viral genotype, viral load, age and gender to the responsiveness of interferon.

Genetic predisposition of responsiveness to therapy for chronic hepatitis C.

BACKGROUND: A combination of interferon-alpha (IFN-alpha) and ribavirin has been the choice for treating chronic hepatitis C (CHC) patients. It achieves an overall sustained response rate of approximately 50%; however, the treatment takes 6-12 months and often brings significant adverse reactions to some patients. It would therefore be beneficial to include a pretreatment evaluation in order to maximize the efficacy. In addition to viral genotypes, we hypothesize that patient genotypes might also be useful for the prediction of treatment response. METHODS: We retrospectively analyzed the genetic differences of CHC patients that are associated with IFN/ribavirin responses. The DNA polymorphisms among 195 sustained responders and 122 nonresponders of CHC patients of Taiwanese origin were compared. Statistical and algorithmic methods were used to select the genes associated with drug response and single nucleotide polymorphisms (SNPs) that permitted the construction of a predictive model. RESULTS: Association studies and haplotype reconstruction revealed selection of seven genes: adenosine deaminase, RNA-specific (ADAR), caspase 5, apoptosis-related cysteine peptidase (CASP5), fibroblast growth factor 1 (FGF1), interferon consensus sequence binding protein 1 (ICSBP1), interferon-induced protein 44 (IFI44), transporter 2, ATP-binding cassette, subfamily B (TAP2) and transforming growth factor, beta receptor associated protein 1 (TGFBRAP1) for the responsiveness trait. Based on confirmed linkage disequilibrium block in the population, a minimal set of 26 SNPs in the seven selected genes was inferred. To predict treatment outcome, a multiple logistic regression model was constructed using susceptible genotypes of SNPs. The performance of the resultant model had a sensitivity of 68.2% and specificity of 60.7% on 317 CHC patients treated with IFN-combined therapy. In addition, a prediction model with both the host genetic and viral genotype information was also constructed which enhanced the performance with a sensitivity of 80.7% and specificity of 67.2%. CONCLUSIONS: A genetic model was constructed to predict outcomes of the combination therapy in CHC patients with high sensitivity and specificity. Results also provide a possible process of selecting targets for predicting treatment outcomes and the basis for developing pharmacogenetic tests.

An algorithm for model construction and its applications to pharmacogenomic studies.

A model depicts the relationship between clinical phenotypes and genotypes on a set of genetic polymorphisms. After the model is constructed and validated, it may be used to predict clinical phenotypes such as traits of complex diseases. A pharmacogenomic model is used to predict the efficacies or adverse drug reactions of a medication. The construction of a model is a challenging task. This is because a single-locus polymorphism does not contain enough information to stratify patients in general, given the complex biological mechanisms involved. An exhaustive search for the correct combination of genotypes across multiple loci is, however, computationally infeasible. We are, thus, motivated to propose a novel algorithm for the construction of models using the multiple single-nucleotide polymorphism (SNP) information in diplotype forms. This algorithm utilizes the techniques of genetic algorithms and Boolean algebra (GABA). The proposed algorithm is tested on simulated data, as well as real genotype datasets of chronic hepatitis C patients treated with interferon-combined therapy. A model for predicting the treatment efficacy is constructed and validated. The results showed that the proposed algorithm is very effective in deriving models comprising multiple SNPs.