Pattern-recognition techniques with haplotype analysis in pharmacogenomics

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Pharmacogenomic Biomarkers and Their Applications in Psychiatry

Realizing the promise of precision medicine in psychiatry is a laudable and beneficial endeavor, since it should markedly reduce morbidity and mortality and, in effect, alleviate the economic and social burden of psychiatric disorders. This review aims to summarize important issues on pharmacogenomics in psychiatry that have laid the foundation towards personalized pharmacotherapy and, in a broader sense, precision medicine. We present major pharmacogenomic biomarkers and their applications in a variety of psychiatric disorders, such as depression, attention-deficit/hyperactivity disorder (ADHD), narcolepsy, schizophrenia, and bipolar disorder. In addition, we extend the scope into epilepsy, since antiepileptic drugs are widely used to treat psychiatric disorders, although epilepsy is conventionally considered to be a neurological disorder.

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

Pharmacogenomics And Hypertension: Current Insights

Hypertension is a multifactorial disease that affects approximately one billion subjects worldwide and is a major risk factor associated with cardiovascular events, including coronary heart disease and cerebrovascular accidents. Therefore, adequate blood pressure control is important to prevent these events, reducing premature mortality and disability. However, only one third of patients have the effective control of blood pressure, despite several classes of antihypertensive drugs available. These disappointing outcomes may be at least in part explained by interpatient variability in drug response due to genetic polymorphisms. To address the effects of genetic polymorphisms on blood pressure responses to the antihypertensive drug classes, studies have applied candidate genes and genome wide approaches. More recently, a third approach that considers gene-gene interactions has also been applied in hypertension pharmacogenomics. In this article, we carried out a comprehensive review of recent findings on the pharmacogenomics of antihypertensive drugs, including diuretics, β-blockers, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, and calcium channel blockers. We also discuss the limitations and inconsistences that have been found in hypertension pharmacogenomics and the challenges to implement this valuable approach in clinical practice.

Machine learning and systems genomics approaches for multi-omics data

In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

Genome-wide association studies in pharmacogenomics of antidepressants

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.

Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy

Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy.

Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population

An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.

Dialysis modality-dependent changes in serum metabolites: accumulation of inosine and hypoxanthine in patients on haemodialysis

BACKGROUND

The body metabolism of patients with end-stage renal disease may be altered in response to long-term dialysis treatment. Moreover, the pattern of serum metabolites could change depending on the type of dialysis modality used. However, dialysis modality-dependent changes in serum metabolites are poorly understood. Our aim was to profile comprehensively serum metabolites by exploiting a novel method of (1)H-NMR-based metabonomics and identify the differences in metabolite patterns in subjects receiving haemodialysis (HD) and peritoneal dialysis (PD).

METHODS

Anuric and non-diabetic HD patients were matched to PD patients for age, sex and dialysis duration. Accurate concentrations of serum metabolites were determined using the target-profiling procedure, and differences in the levels of metabolites were compared using multivariate analysis.

RESULTS

Principal Components Analysis score plots showed that the metabolic patterns could be discriminated by dialysis modalities. Hypoxanthine and inosine were present only with HD, whereas serum xanthine oxidase activity and uric acid levels were not different. In contrast, PD was associated with higher levels of lactate, glucose, maltose, pyruvate, succinate, alanine, and glutamate linked to glucose metabolism and the tri-carboxylic acid cycle. Maltose appeared only in patients using icodextrin solution for PD. Known uraemic retention solutes such as urea, creatinine, myo-inositol and trimethylamine-N-oxide were increased in both dialysis groups.

CONCLUSIONS

Metabonomics shows apparent differences in the profiles of serum metabolites between HD and PD, which were influenced by dialysis-related processes. Inosine and hypoxanthine are present only in HD patients, which is likely to represent more hypoxic and oxidative stress.

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 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.

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.

Interaction of serotonin-related genes affects short-term antidepressant response in major depressive disorder

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.

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

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.

A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome

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.

Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders

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.

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.

Identification of significant genes in genomics using Bayesian variable selection methods

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.

Pharmacogenomics with antidepressants in the STAR*D study

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.

Defining targets for investigating the pharmacogenomics of adverse drug reactions to antifungal agents

Adverse drug reactions (ADRs) associated with antifungal therapy are major problems in patients with invasive fungal infections. Whether by clinical history or patterns of genetic variation, the identification of patients at risk for ADRs should result in improved outcomes while minimizing deleterious side effects. A major contributing factor to ADRs with antifungal agents relates to drug distribution, metabolism and excretion. Genetic variation in key genes can alter the structure and expression of genes and gene products (e.g., proteins). Thus far, the effort has focused on identifying polymorphisms with either empirical or predicted in silico functional consequences; the best candidate genes encode phase I and II drug-metabolizing enzymes (e.g., CYP2C19 and N-acetyltransferase), plasma proteins (albumin and lipoproteins) and drug transporters (P-glycoprotein and multidrug resistance proteins), which can affect the disposition of antifungal agents, eventually leading to dose-dependent (type A) toxicity. Less is known regarding the key genes that interact with antifungal agents, resulting in idiosyncratic (type B) ADRs. The possible role of certain gene products and genetic polymorphisms in the toxicities of antifungal agents are discussed in this review. The preliminary data address the following: low-density lipoproteins and cholesteryl ester transfer protein in amphotericin B renal toxicity; toll-like receptor 1 and 2 in amphotericin B infusion-related ADRs; phosphodiesterase 6 in voriconazole visual adverse events; flavin-containing monooxygenase, glutathione transferases and multidrug resistance proteins 1 and 2 in ketoconazole and terbinafine hepatotoxicity; CYP enzymes and P-glycoprotein in drug interactions between azoles and coadministered medications; multidrug resistance proteins 8 and 9 on 5-flucytosine bone marrow toxicity; and mast cell activation in caspofungin histamine release. This will focus on high-priority candidate genes, which could provide a starting point for molecular studies to elucidate the potential mechanisms for understanding toxicity associated with antifungal drugs as well as identifying candidate genes for large population prospective genetic association studies.

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.

Haplotype-association analysis

Association methods based on linkage disequilibrium (LD) offer a promising approach for detecting genetic variations that are responsible for complex human diseases. Although methods based on individual single nucleotide polymorphisms (SNPs) may lead to significant findings, methods based on haplotypes comprising multiple SNPs on the same inherited chromosome may provide additional power for mapping disease genes and also provide insight on factors influencing the dependency among genetic markers. Such insights may provide information essential for understanding human evolution and also for identifying cis-interactions between two or more causal variants. Because obtaining haplotype information directly from experiments can be cost prohibitive in most studies, especially in large scale studies, haplotype analysis presents many unique challenges. In this chapter, we focus on two main issues: haplotype inference and haplotype-association analysis. We first provide a detailed review of methods for haplotype inference using unrelated individuals as well as related individuals from pedigrees. We then cover a number of statistical methods that employ haplotype information in association analysis. In addition, we discuss the advantages and limitations of different methods.