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author:("Hwang, quchi")
1.  Association of the C825T polymorphism in the GNB3 gene with obesity and metabolic phenotypes in a Taiwanese population 
Genes & Nutrition  2012;8(1):137-144.
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).
doi:10.1007/s12263-012-0304-8
PMCID: PMC3534990  PMID: 22791279
G-protein; Metabolic phenotypes; Obesity; Single nucleotide polymorphisms
2.  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
doi:10.1186/2043-9113-1-3
PMCID: PMC3143900  PMID: 21884624
3.  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.
PMCID: PMC3170005  PMID: 21918625
chronic hepatitis C; artificial neural networks; interferon; pharmacogenomics; ribavirin; single nucleotide polymorphisms

Results 1-3 (3)