Genome-wide association studies in pharmacogenomics

Genome-Wide Association Studies of Chemotherapeutic Toxicities: Genomics of Inequality

With an estimated global population of cancer survivors exceeding 32 million and growing, there is a heightened awareness of the long-term toxicities resulting from cancer treatments and their impact on quality of life. Unexplained heterogeneity in the persistence and development of toxicities, as well as an incomplete understanding of their mechanisms, have generated a growing need for the identification of predictive pharmacogenomic markers. Early studies addressing this need used a candidate gene approach; however, over the last decade, unbiased and comprehensive genome-wide association studies (GWAS) have provided markers of phenotypic risk and potential targets to explore the mechanistic and regulatory pathways of biological functions associated with chemotherapeutic toxicity. In this review, we provide the current status of GWAS of chemotherapeutic toxicities with an emphasis on examining the ancestral diversity of the representative cohorts within these studies. Persistent calls to incorporate both ancestrally diverse and/or admixed populations into genomic efforts resulted in a recent rise in the number of studies utilizing cohorts of East Asian descent; however, few pharmacogenomic studies to date include cohorts of African, Indigenous American, Southwest Asian, and admixed populations. Through comprehensively evaluating sample size, composition by ancestry, genome-wide significant variants, and population-specific minor allele frequencies as reported by HapMap/dbSNP using NCBI PubMed and the NHGRI-EBI GWAS Catalog, we illustrate how allele frequencies and effect sizes tend to vary among individuals of differing ancestries. In an era of personalized medicine, the lack of diversity in genome-wide studies of anticancer agent toxicity may contribute to the health disparity gap. Clin Cancer Res; 23(15); 4010-9. ©2017 AACR.

Genome-wide association study of therapeutic opioid dosing identifies a novel locus upstream of OPRM1

Opioids are very effective analgesics, but they are also highly addictive. Methadone is used to treat opioid dependence (OD), acting as a selective agonist at the μ-opioid receptor encoded by the gene OPRM1. Determining the optimal methadone maintenance dose is time consuming; currently, no biomarkers are available to guide treatment. In methadone-treated OD subjects drawn from a case and control sample, we conducted a genome-wide association study of usual daily methadone dose. In African-American (AA) OD subjects (n=383), we identified a genome-wide significant association between therapeutic methadone dose (mean=68.0 mg, s.d.=30.1 mg) and rs73568641 (P=2.8 × 10-8), the nearest gene (306 kilobases) being OPRM1. Each minor (C) allele corresponded to an additional ~20 mg day-1 of oral methadone, an effect specific to AAs. In European-Americans (EAs) (n=1027), no genome-wide significant associations with methadone dose (mean=77.8 mg, s.d.=33.9 mg) were observed. In an independent set of opioid-naive AA children being treated for surgical pain, rs73568641-C was associated with a higher required dose of morphine (n=241, P=3.9 × 10-2). Similarly, independent genomic loci previously shown to associate with higher opioid analgesic dose were associated with higher methadone dose in the OD sample (AA and EA: n=1410, genetic score P=1.3 × 10-3). The present results in AAs indicate that genetic variants influencing opioid sensitivity across different clinical settings could contribute to precision pharmacotherapy for pain and addiction.

Genome-Wide Association Studies for Idiosyncratic Drug-Induced Hepatotoxicity: Looking Back-Looking Forward to Next-Generation Innovation

Idiosyncratic drug-induced hepatotoxicity is a formidable challenge for rational drug discovery and development, as well as the science of personalized medicine. There is evidence that hereditary factors, in part, contribute to drug toxicity. This expert analysis and review offer the insights gained, and the challenges ahead, for genome-wide association studies (GWASs) of idiosyncratic drug-induced hepatotoxicity. Published articles on genome-wide and subsequent replication studies were systematically searched in the PubMed electronic database. We found that the genetic risk variants that were identified genome-wide, and replication confirmed, are mainly related to polymorphisms in the human leukocyte antigen (HLA) region that include HLA-DQB1*06:02 for amoxicillin-clavulanate, HLA-B*57:01 for flucloxacillin, HLA-DRB1*15:01 for lumiracoxib, and HLA-DRB1*07:01 for lapatinib and ximelagatran-induced hepatotoxicity. Additionally, polymorphisms in ST6 β-galactosamide α-2, 6-sialyltranferase-1 (ST6GAL1), which plays a role in systemic inflammatory response, and variants in intron of family with sequence similarity-65 member-B (FAM65B) that play roles in liver inflammation displayed association with flucloxacillin and antituberculosis drug-induced hepatotoxicity, respectively. Taken together, these GWAS findings offer molecular leads on the central role that the immune system plays in idiosyncratic drug-induced hepatotoxicity. We conclude the expert review with a brief discussion of the salient challenges ahead. These include, for example, the need for discursive discovery paradigms that incorporate alternating GWASs and candidate gene studies, as well as the study of the environtome, the entire complement of environmental factors, including science and innovation policies that enact on global society and the human host, and by extension, on susceptibility for idiosyncratic drug-induced hepatotoxicity.

Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies

Genome-wide association study (GWAS) entails examining a large number of single nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying a variable selection problem in the high dimensional dataset. Although many single-locus GWAS approaches under polygenic background and population structure controls have been widely used, some significant loci fail to be detected. In this study, we used an iterative modified-sure independence screening (ISIS) approach in reducing the number of SNPs to a moderate size. Expectation-Maximization (EM)-Bayesian least absolute shrinkage and selection operator (BLASSO) was used to estimate all the selected SNP effects for true quantitative trait nucleotide (QTN) detection. This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). To further demonstrate the new method, six flowering time traits in Arabidopsis thaliana were re-analyzed by four methods (New method, EMMA, FarmCPU, and mrMLM). As a result, the new method identified most previously reported genes. Therefore, the new method is a good alternative for multi-locus GWAS.

Genome-wide association study is concerned with the associations between markers and traits of interest so as to identify all the significantly associated markers. In genome-wide association studies, hundreds of thousands of markers are genotyped for several hundreds of individuals. Usually, only a very minor subset of these markers is associated with the trait. Most penalization methods fail when the number of markers is much larger than the sample size. Based on this fact, we have developed an algorithm that proceeds in two stages. In the first stage (screening), we reduced the number of markers via correlation learning to a moderate size. We then used a moderate-scale variable selection method to select variables in the reduced model. Conditional on the selected variables, we repeated the screening procedure and chose another set of variables. In the second stage (estimation), all the above-selected variables are accurately estimated in a multi-locus model. Our approach is simple, accurate in estimation, fast and shows high statistical power of detecting relevant markers on simulated data. We have also used this method to identify relevant genes in real data analysis. We recommend our approach for conducting a multi-locus genome-wide association study.

Propelling the paradigm shift from reductionism to systems nutrition

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

Steroid-induced ocular hypertension/glaucoma: Focus on pharmacogenomics and implications for precision medicine

Elevation of intraocular pressure (IOP) due to therapeutic use of glucocorticoids is called steroid-induced ocular hypertension (SIOH); this can lead to steroid-induced glaucoma (SIG). Glucocorticoids initiate signaling cascades ultimately affecting expression of hundreds of genes; this provides the potential for a highly personalized pharmacological response. Studies attempting to define genetic risk factors were undertaken early in the history of glucocorticoid use, however scientific tools available at that time were limited and progress stalled. In contrast, significant advances were made over the ensuing years in defining disease pathophysiology. As the genomics age emerged, it appeared the time was right to renew investigation into genetics. Pharmacogenomics is an unbiased discovery approach, not requiring an underlying hypothesis, and provides a way to pinpoint clinically significant genes and pathways that could not have been discovered any other way. Results of the first genome-wide association study to identify polymorphisms associated with SIOH, and follow-up on two novel genes linked to the disorder, GPR158 and HCG22, is discussed in the second half of the article. However, knowledge of genetic variants determining response to steroids in the eye also has value in its own right as a predictive and diagnostic tool. This article concludes with a discussion of how the Precision Medicine Initiative®, announced by U.S. President Obama in his 2015 State of the Union address, is beginning to touch the practice of ophthalmology. It is argued that SIOH/SIG may provide one of the next opportunities for effective application of precision medicine.

Impact of New Genomic Technologies on Understanding Adverse Drug Reactions

It is well established that variations in genes can alter the pharmacokinetic and pharmacodynamic profile of a drug and immunological responses to it. Early advances in pharmacogenetics were made with traditional genetic techniques such as functional cloning of genes using knowledge gained from purified proteins, and candidate gene analysis. Over the past decade, techniques for analysing the human genome have accelerated greatly as knowledge and technological capabilities have grown. These techniques were initially focussed on understanding genetic factors of disease, but increasingly they are helping to clarify the genetic basis of variable drug responses and adverse drug reactions (ADRs). We examine genetic methods that have been applied to the understanding of ADRs, review the current state of knowledge of genetic factors that influence ADR development, and discuss how the application of genome-wide association studies and next-generation sequencing approaches is supporting and extending existing knowledge of pharmacogenetic processes leading to ADRs. Such approaches have identified single genes that are major contributing genetic risk factors for an ADR, (such as flucloxacillin and drug-induced liver disease), making pre-treatment testing a possibility. They have contributed to the identification of multiple genetic determinants of a single ADR, some involving both pharmacologic and immunological processes (such as phenytoin and severe cutaneous adverse reactions). They have indicated that rare genetic variants, often not previously reported, are likely to have more influence on the phenotype than common variants that have been traditionally tested for. The problem of genotype/phenotype discordance affecting the interpretation of pharmacogenetic screening and the future of genome-based testing applied to ADRs are also discussed.

Genomics era and complex disorders: Implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers

The Human Genome Project (HGP) has identified millions of single nucleotide polymorphisms (SNPs) and their association with several diseases, apart from successfully characterizing the Mendelian/monogenic diseases. However, the dissection of precise etiology of complex genetic disorders still poses a challenge for human geneticists. This review outlines the landmark results of genome-wide association studies (GWAS) with respect to major complex diseases - Coronary artery disease (CAD), type 2 diabetes mellitus (T2DM), and predominant cancers. A brief account on the current Indian scenario is also given. All the relevant publications till mid-2015 were accessed through web databases such as PubMed and Google. Several databases providing genetic information related to these diseases were tabulated and in particular, the list of the most significant SNPs identified through GWAS was made, which may be useful for designing studies in functional validation. Post-GWAS implications and emerging concepts such as epigenomics and pharmacogenomics were also discussed.

Targeted Next-Generation Sequencing for Comprehensive Genetic Profiling of Pharmacogenes

Phenotypic differences in drug responses have been associated with known pharmacogenomic loci, but many remain to be characterized. Therefore, we developed next-generation sequencing (NGS) panels to enable broad and unbiased inspection of genes that are involved in pharmacokinetics (PKs) and pharmacodynamics (PDs). These panels feature repetitively optimized probes to capture up to 114 PK/PD-related genes with high coverage (99.6%) and accuracy (99.9%). Sequencing of a Korean cohort (n = 376) with the panels enabled profiling of actionable variants as well as rare variants of unknown functional consequences. Notably, variants that occurred at low frequency were enriched with likely protein-damaging variants and previously unreported variants. Furthermore, in vitro evaluation of four pharmacogenes, including cytochrome P450 2C19 (CYP2C19), confirmed that many of these rare variants have considerable functional impact. The present study suggests that targeted NGS panels are readily applicable platforms to facilitate comprehensive profiling of pharmacogenes, including common but also rare variants that warrant screening for personalized medicine.

Landscape of warfarin and clopidogrel pharmacogenetic variants in Qatari population from whole exome datasets

AIM

Pharmacogenetic landscapes of commonly used antiplatelet drugs, warfarin and clopidogrel have been studied in-depth in many countries. However, there is a paucity of data to understand their patterns in the Arab populations.

MATERIALS & METHODS

We analyzed the whole exome sequencing datasets of 100 Qatar individuals available in public domain with this perspective.

RESULTS

We characterized the allelic distribution of variants routinely tested for warfarin and clopidogrel. We additionally evaluated the population stratification and its effect on allele frequency distribution. Our analysis points to ethnic differences in the frequency distribution even for the small population studied.

CONCLUSION

This is one of the first and most comprehensive pharmacogenetic maps of variants associated with warfarin and clopidogrel for an Arab population, which can help tailor the drug dosage to the population.

Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist-hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.

Pharmacogenetics of Drug-Induced QT Interval Prolongation: An Update

A prolonged QT interval is an important risk factor for ventricular arrhythmias and sudden cardiac death. QT prolongation can be caused by drugs. There are multiple risk factors for drug-induced QT prolongation, including genetic variation. QT prolongation is one of the most common reasons for withdrawal of drugs from the market, despite the fact that these drugs may be beneficial for certain patients and not harmful in every patient. Identifying genetic variants associated with drug-induced QT prolongation might add to tailored pharmacotherapy and prevent beneficial drugs from being withdrawn unnecessarily. In this review, our objective was to provide an overview of the genetic background of drug-induced QT prolongation, distinguishing pharmacokinetic and pharmacodynamic pathways. Pharmacokinetic-mediated genetic susceptibility is mainly characterized by variation in genes encoding drug-metabolizing cytochrome P450 enzymes or drug transporters. For instance, the P-glycoprotein drug transporter plays a role in the pharmacokinetic susceptibility of drug-induced QT prolongation. The pharmacodynamic component of genetic susceptibility is mainly characterized by genes known to be associated with QT interval duration in the general population and genes in which the causal mutations of congenital long QT syndromes are located. Ethnicity influences susceptibility to drug-induced QT interval prolongation, with Caucasians being more sensitive than other ethnicities. Research on the association between pharmacogenetic interactions and clinical endpoints such as sudden cardiac death is still limited. Future studies in this area could enable us to determine the risk of arrhythmias more adequately in clinical practice.

Evidence for extensive pleiotropy among pharmacogenes

AIM

We sought to identify potential pleiotropy involving pharmacogenes.

METHODS

We tested 184 functional variants in 34 pharmacogenes for associations using a custom grouping of International Classification and Disease, Ninth Revision billing codes extracted from deidentified electronic health records of 6892 patients.

RESULTS

We replicated several associations including ABCG2 (rs2231142) and gout (p = 1.73 × 10(-7); odds ratio [OR]: 1.73; 95% CI: 1.40-2.12); and SLCO1B1 (rs4149056) and jaundice (p = 2.50 × 10(-4); OR: 1.67; 95% CI: 1.27-2.20).

CONCLUSION

In this systematic screen for phenotypic associations with functional variants, several novel genotype-phenotype combinations also achieved phenome-wide significance, including SLC15A2 rs1143672 and renal osteodystrophy (p = 2.67 × 10(-) (6); OR: 0.61; 95% CI: 0.49-0.75).

Genetic polymorphisms of pharmacogenomic VIP variants in the Mongol of Northwestern China

Background

Within a population, the differences of pharmacogenomic variant frequencies may produce diversities in drug efficacy, safety, and the risk associated with adverse drug reactions. With the development of pharmacogenomics, widespread genetic research on drug metabolism has been conducted on major populations, but less is known about minorities.

Results

In this study, we recruited 100 unrelated, healthy Mongol adults from Xinjiang and genotyped 85 VIP variants from the PharmGKB database. We compared our data with eleven populations listed in 1000 genomes project and HapMap database. We used χ² tests to identify significantly different loci between these populations. We downloaded SNP allele frequencies from the ALlele FREquency Database to observe the global genetic variation distribution for these specific loci. And then we used Structure software to perform the genetic structure analysis of 12 populations.

Conclusions

Our results demonstrated that different polymorphic allele frequencies exist between different nationalities,and indicated Mongol is most similar to Chinese populations, followed by JPT. This information on the Mongol population complements the existing pharmacogenomic data and provides a theoretical basis for screening and therapy in the different ethnic groups within Xinjiang.

Clinical association between pharmacogenomics and adverse drug reactions

Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual's response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.

Germline polymorphisms in an enhancer of PSIP1 are associated with progression-free survival in epithelial ovarian cancer

Women with epithelial ovarian cancer (EOC) are usually treated with platinum/taxane therapy after cytoreductive surgery but there is considerable inter-individual variation in response. To identify germline single-nucleotide polymorphisms (SNPs) that contribute to variations in individual responses to chemotherapy, we carried out a multi-phase genome-wide association study (GWAS) in 1,244 women diagnosed with serous EOC who were treated with the same first-line chemotherapy, carboplatin and paclitaxel. We identified two SNPs (rs7874043 and rs72700653) in TTC39B (best P=7x10-5, HR=1.90, for rs7874043) associated with progression-free survival (PFS). Functional analyses show that both SNPs lie in a putative regulatory element (PRE) that physically interacts with the promoters of PSIP1, CCDC171 and an alternative promoter of TTC39B. The C allele of rs7874043 is associated with poor PFS and showed increased binding of the Sp1 transcription factor, which is critical for chromatin interactions with PSIP1. Silencing of PSIP1 significantly impaired DNA damage-induced Rad51 nuclear foci and reduced cell viability in ovarian cancer lines. PSIP1 (PC4 and SFRS1 Interacting Protein 1) is known to protect cells from stress-induced apoptosis, and high expression is associated with poor PFS in EOC patients. We therefore suggest that the minor allele of rs7874043 confers poor PFS by increasing PSIP1 expression.

Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study

We propose a multiple estimation adjustment (MEA) method to correct effect overestimation due to selection bias from a hypothesis-generating study (HGS) in pharmacogenetics. MEA uses a hierarchical Bayesian approach to model individual effect estimates from maximal likelihood estimation (MLE) in a region jointly and shrinks them toward the regional effect. Unlike many methods that model a fixed selection scheme, MEA capitalizes on local multiplicity independent of selection. We compared mean square errors (MSEs) in simulated HGSs from naive MLE, MEA and a conditional likelihood adjustment (CLA) method that model threshold selection bias. We observed that MEA effectively reduced MSE from MLE on null effects with or without selection, and had a clear advantage over CLA on extreme MLE estimates from null effects under lenient threshold selection in small samples, which are common among 'top' associations from a pharmacogenetics HGS.

Genome-wide association study identifies candidate loci associated with postoperative fentanyl requirements after laparoscopic-assisted colectomy

AIMS

Opioids are widely used as effective analgesics, but opioid sensitivity is well known to vary widely among individuals and the underlying genetic factors are not fully understood, thus hampering efficient pain treatment. We explored the genetic factors that contribute to individual differences in opioid sensitivity by performing a genome-wide association study.

METHODS

We conducted a multistage genome-wide association study in subjects who underwent laparoscopic-assisted colectomy (LAC).

RESULTS

A nonsynonymous SNP in the LAMB3 gene region, rs2076222, was strongly associated with postoperative opioid requirements. The C allele of this best-candidate SNP was associated with lower opioid sensitivity and/or higher pain sensitivity in the patient subjects.

CONCLUSION

Our findings provide valuable information for personalized pain treatment after LAC, in which the C allele of the rs2076222 SNP is associated with lower opioid sensitivity and requires more opioid analgesic after LAC.

Uncovering drug-responsive regulatory elements

Nucleotide changes in gene regulatory elements can have a major effect on interindividual differences in drug response. For example, by reviewing all published pharmacogenomic genome-wide association studies, we show here that 96.4% of the associated single nucleotide polymorphisms reside in noncoding regions. We discuss how sequencing technologies are improving our ability to identify drug response-associated regulatory elements genome-wide and to annotate nucleotide variants within them. We highlight specific examples of how nucleotide changes in these elements can affect drug response and illustrate the techniques used to find them and functionally characterize them. Finally, we also discuss challenges in the field of drug-responsive regulatory elements that need to be considered in order to translate these findings into the clinic.

Asthma Pharmacogenomics: 2015 Update

There is evidence that genetic factors are implicated in the observed differences in therapeutic responses to the common classes of asthma therapy such as β2-agonists, corticosteroids, and leukotriene modifiers. Pharmacogenomics explores the roles of genetic variation in drug response and continues to be a field of great interest in asthma therapy. Prior studies have focused on candidate genes and recently emphasized genome-wide association analyses. Newer integrative omics and system-level approaches have recently revealed novel understanding of drug response pathways. However, the current known genetic loci only account for a fraction of variability in drug response and ongoing research is needed. While the field of asthma pharmacogenomics is not yet fully translatable to clinical practice, ongoing research should hopefully achieve this goal in the near future buttressed by the recent precision medicine efforts in the USA and worldwide.

Genotype-based clinical trials in cardiovascular disease

Consensus practice guidelines and the implementation of clinical therapeutic advances are usually based on the results of large, randomized clinical trials (RCTs). However, RCTs generally inform us on an average treatment effect for a presumably homogeneous population, but therapeutic interventions rarely benefit the entire population targeted. Indeed, multiple RCTs have demonstrated that interindividual variability exists both in drug response and in the development of adverse effects. The field of pharmacogenomics promises to deliver the right drug to the right patient. Substantial progress has been made in this field, with advances in technology, statistical and computational methods, and the use of cell and animal model systems. However, clinical implementation of pharmacogenetic principles has been difficult because RCTs demonstrating benefit are lacking. For patients, the potential benefits of performing such trials include the individualization of therapy to maximize efficacy and minimize adverse effects. These trials would also enable investigators to reduce sample size and hence contain costs for trial sponsors. Multiple ethical, legal, and practical issues need to be considered for the conduct of genotype-based RCTs. Whether pre-emptive genotyping embedded in electronic health records will preclude the need for performing genotype-based RCTs remains to be seen.

Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits

It has been suggested that pharmacogenomic phenotypes are influenced by genetic variants with larger effect sizes than other phenotypes, such as complex disease risk. This is presumed to reflect the fact that relevant environmental factors (drug exposure) are appropriately measured and taken into account. To test this hypothesis, we performed a systematic comparison of effect sizes between pharmacogenomic and non-pharmacogenomic phenotypes across all genome-wide association studies (GWAS) reported in the NHGRI GWAS catalog. We found significantly larger effect sizes for studies focused on pharmacogenomic phenotypes, as compared to complex disease risk, morphological phenotypes, and endophenotypes. We found no significant differences in effect sizes between pharmacogenomic studies focused on adverse events versus those focused on drug efficacy. Furthermore, we found that this pattern persists among sample size-matched studies, suggesting that this pattern does not reflect over-estimation of effect sizes due to smaller sample sizes in pharmacogenomic studies.

Progress in understanding the genomic basis for adverse drug reactions: a comprehensive review and focus on the role of ethnicity

A major goal of the field of pharmacogenomics is to identify the genomic causes of serious adverse drug reactions (ADRs). Increasingly, genome-wide association studies (GWAS) have been used to achieve this goal. In this article, we review recent progress in the identification of genetic variants associated with ADRs using GWAS and discuss emerging themes from these studies. We also compare aspects of GWAS for ADRs to GWAS for common diseases. In the second part of the article, we review progress in performing pharmacogenomic research in multi-ethnic populations and discuss the challenges and opportunities of investigating genetic causes of ADRs in ethnically diverse patient populations.

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs

INTRODUCTION

Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study.

AREAS COVERED

The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits.

EXPERT OPINION

Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years.

BAYESIAN GROUP LASSO FOR NONPARAMETRIC VARYING-COEFFICIENT MODELS WITH APPLICATION TO FUNCTIONAL GENOME-WIDE ASSOCIATION STUDIES

Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or fGWAS. Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. The fGWAS model, equipped with Bayesian group lassso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.

The current and potential impact of genetics and genomics on neuropsychopharmacology

One justification for the major scientific and financial investments in genetic and genomic studies in medicine is their therapeutic potential, both for revealing novel targets for drugs which treat the disease process, as well as allowing for more effective and safe use of existing medications. This review considers the extent to which this promise has yet been realised within psychopharmacology, how things are likely to develop in the foreseeable future, and the key issues involved. It draws primarily on examples from schizophrenia and its treatments. One observation is that there is evidence for a range of genetic influences on different aspects of psychopharmacology in terms of discovery science, but far less evidence that meets the standards required before such discoveries impact upon clinical practice. One reason is that results reveal complex genetic influences that are hard to replicate and usually of very small effect. Similarly, the slow progress being made in revealing the genes that underlie the major psychiatric syndromes hampers attempts to apply the findings to identify novel drug targets. Nevertheless, there are some intriguing positive findings of various kinds, and clear potential for genetics and genomics to play an increasing and major role in psychiatric drug discovery.

HLA-B*59:01: a marker for Stevens-Johnson syndrome/toxic epidermal necrolysis caused by methazolamide in Han Chinese

Methazolamide is an intraocular pressure-lowering drug that is used in the treatment of glaucoma and other ophthalmologic abnormalities. The use of methazolamide has been shown to cause Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) in patients of Asian ancestry. Methazolamide-induced SJS/TEN is associated with the presence of HLA-B59 serotype/HLA-B*59:01 in Korean and Japanese populations. To better understand the genetic risk factors for these adverse reactions in the Han Chinese population, we characterized the HLA class I genotypes of eight Chinese patients with methazolamide-induced SJS/TEN from 2008 to 2014. The frequency of HLA-B*59:01 was 87.5% (7/8) in the case patients, which was significantly different from 0% (0/30) in the methazolamide-tolerant patients (odds ratio (OR)=305.0; P=6.3 × 10(-7)) and 0.35% (1/283) in healthy subjects from the human major histocompatibility complex database (OR=1974.0; P=2.0 × 10(-12)). HLA-C*01:02, which is closely linked to HLA-B*59:01, had a weaker but notable association with methazolamide-induced SJS/TEN compared with the tolerant controls (OR=12.1; P=0.016) and general population (OR=15.5; P=2.0 × 10(-3)). The distribution of the HLA-B*59:01-C*01:02 haplotype was also significantly different in cases and controls. This study demonstrated a strong association between HLA-B*59:01 and methazolamide-induced SJS/TEN in the Han Chinese population for the first time. Pretherapy screening for HLA-B*59:01 would be useful to reduce the risk of methazolamide-induced SJS/TEN.

Identification of a Novel Mucin Gene HCG22 Associated With Steroid-Induced Ocular Hypertension

PURPOSE

The pathophysiology of ocular hypertension (OH) leading to primary open-angle glaucoma shares many features with a secondary form of OH caused by treatment with glucocorticoids, but also exhibits distinct differences. In this study, a pharmacogenomics approach was taken to discover candidate genes for this disorder.

METHODS

A genome-wide association study was performed, followed by an independent candidate gene study, using a cohort enrolled from patients treated with off-label intravitreal triamcinolone, and handling change in IOP as a quantitative trait.

RESULTS

An intergenic quantitative trait locus (QTL) was identified at chromosome 6p21.33 near the 5' end of HCG22 that attained the accepted statistical threshold for genome-level significance. The HCG22 transcript, encoding a novel mucin protein, was expressed in trabecular meshwork cells, and expression was stimulated by IL-1, and inhibited by triamcinolone acetate and TGF-β. Bioinformatic analysis defined the QTL as an approximately 4 kilobase (kb) linkage disequilibrium block containing 10 common single nucleotide polymorphisms (SNPs). Four of these SNPs were identified in the National Center for Biotechnology Information (NCBI) GTEx eQTL browser as modifiers of HCG22 expression. Most are predicted to disrupt or improve motifs for transcription factor binding, the most relevant being disruption of the glucocorticoid receptor binding motif. A second QTL was identified within the predicted signal peptide of the HCG22 encoded protein that could affect its secretion. Translation, O-glycosylation, and secretion of the predicted HCG22 protein was verified in cultured trabecular meshwork cells.

CONCLUSIONS

Identification of two independent QTLs that could affect expression of the HCG22 mucin gene product via two different mechanisms (transcription or secretion) is highly suggestive of a role in steroid-induced OH.

Human experimental endotoxemia in modeling the pathophysiology, genomics, and therapeutics of innate immunity in complex cardiometabolic diseases

Inflammation is a fundamental feature of several complex cardiometabolic diseases. Indeed, obesity, insulin resistance, metabolic dyslipidemia, and atherosclerosis are all closely linked inflammatory states. Increasing evidence suggests that the infectious, biome-related, or endogenous activation of the innate immune system may contribute to the development of metabolic syndrome and cardiovascular disease. Here, we describe the human experimental endotoxemia model for the specific study of innate immunity in understanding further the pathogenesis of cardiometabolic disease. In a controlled, experimental setting, administration of an intravenous bolus of purified Escherichia coli endotoxin activates innate immunity in healthy human volunteers. During endotoxemia, changes emerge in glucose metabolism, lipoprotein composition, and lipoprotein functions that closely resemble those observed chronically in inflammatory cardiovascular disease risk states. In this review, we describe the transient systemic inflammation and specific metabolic consequences that develop during human endotoxemia. Such a model provides a controlled induction of systemic inflammation, eliminates confounding, undermines reverse causation, and possesses unique potential as a starting point for genomic screening and testing of novel therapeutics for treatment of the inflammatory underpinning of cardiometabolic disease.

Pharmacodynamic genome-wide association study identifies new responsive loci for glucocorticoid intervention in asthma

Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients' response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10(-7) to 3.924 × 10(-9)), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10(-16) and 5.670 × 10(-11)). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.

Pharmacogenetics of type 2 diabetes mellitus: An example of success in clinical and translational medicine

The pharmacological interventions currently available to control type 2 diabetes mellitus (T2DM) show a wide interindividual variability in drug response, emphasizing the importance of a personalized, more effective medical treatment for each individual patient. In this context, a growing interest has emerged in recent years and has focused on pharmacogenetics, a discipline aimed at understanding the variability in patients’ drug response, making it possible to predict which drug is best for each patient and at what doses. Recent pharmacological and clinical evidences indicate that genetic polymorphisms (or genetic variations) of certain genes can adversely affect drug response and therapeutic efficacy of oral hypoglycemic agents in patients with T2DM, through pharmacokinetic- and/or pharmacodynamic-based mechanisms that may reduce the therapeutic effects or increase toxicity. For example, genetic variants in genes encoding enzymes of the cytochrome P-450 superfamily, or proteins of the ATP-sensitive potassium channel on the beta-cell of the pancreas, are responsible for the interindividual variability of drug response to sulfonylureas in patients with T2DM. Instead, genetic variants in the genes that encode for the organic cation transporters of metformin have been related to changes in both pharmacodynamic and pharmacokinetic responses to metformin in metformin-treated patients. Thus, based on the individual’s genotype, the possibility, in these subjects, of a personalized therapy constitutes the main goal of pharmacogenetics, directly leading to the development of the right medicine for the right patient. Undoubtedly, this represents an integral part of the translational medicine network.

A FAST ALGORITHM FOR DETECTING GENE-GENE INTERACTIONS IN GENOME-WIDE ASSOCIATION STUDIES

With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches. Although many such approaches have been developed and used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few are able to detect gene-gene interactions among different SNPs. Genetic interactions, also known as epistasis, have been recognized to play a pivotal role in contributing to the genetic variation of phenotypic traits. However, because of an extremely large number of SNP-SNP combinations in GWAS, the model dimensionality can quickly become so overwhelming that no prevailing variable selection methods are capable of handling this problem. In this paper, we present a statistical framework for characterizing main genetic effects and epistatic interactions in a GWAS study. Specifically, we first propose a two-stage sure independence screening (TS-SIS) procedure and generate a pool of candidate SNPs and interactions, which serve as predictors to explain and predict the phenotypes of a complex trait. We also propose a rates adjusted thresholding estimation (RATE) approach to determine the size of the reduced model selected by an independence screening. Regularization regression methods, such as LASSO or SCAD, are then applied to further identify important genetic effects. Simulation studies show that the TS-SIS procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as gene-gene interactions. We apply the proposed framework to analyze an ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select 23 active SNPs and 24 active epistatic interactions for the body mass index variation. It shows the capability of our procedure to resolve the complexity of genetic control.

Genomic architecture of pharmacological efficacy and adverse events

The pharmacokinetic and pharmacodynamic disciplines address pharmacological traits, including efficacy and adverse events. Pharmacogenomics studies have identified pervasive genetic effects on treatment outcomes, resulting in the development of genetic biomarkers for optimization of drug therapy. Pharmacogenomics-based tests are already being applied in clinical decision making. However, despite substantial progress in identifying the genetic etiology of pharmacological response, current biomarker panels still largely rely on single gene tests with a large portion of the genetic effects remaining to be discovered. Future research must account for the combined effects of multiple genetic variants, incorporate pathway-based approaches, explore gene-gene interactions and nonprotein coding functional genetic variants, extend studies across ancestral populations, and prioritize laboratory characterization of molecular mechanisms. Because genetic factors can play a key role in drug response, accurate biomarker tests capturing the main genetic factors determining treatment outcomes have substantial potential for improving individual clinical care.

Progress towards the integration of pharmacogenomics in practice

Understanding the role genes and genetic variants play in clinical treatment response continues to be an active area of research with the goal of common clinical use. This goal has developed into today's industry of pharmacogenomics, where new drug-gene relationships are discovered and further characterized, published and then curated into national and international resources for use by researchers and clinicians. These efforts have given us insight into what a pharmacogenomic variant is, and how it differs from human disease variants and common polymorphisms. While publications continue to reveal pharmacogenomic relationships between genes and specific classes of drugs, many challenges remain toward the goal of widespread use clinically. First, the clinical guidelines for pharmacogenomic testing are still in their infancy. Second, sequencing technologies are changing rapidly making it somewhat unclear what genetic data will be available to the clinician at the time of care. Finally, what and when to return data to a patient is an area under constant debate. New innovations such as PheWAS approaches and whole genome sequencing studies are enabling a tsunami of new findings. In this review, pharmacogenomic variants, pharmacogenomic resources, interpretation clinical guidelines and challenges, such as WGS approaches, and the impact of pharmacogenomics on drug development and regulatory approval are reviewed.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

Deciphering next-generation pharmacogenomics: an information technology perspective

In the post-genomic era, the rapid evolution of high-throughput genotyping technologies and the increased pace of production of genetic research data are continually prompting the development of appropriate informatics tools, systems and databases as we attempt to cope with the flood of incoming genetic information. Alongside new technologies that serve to enhance data connectivity, emerging information systems should contribute to the creation of a powerful knowledge environment for genotype-to-phenotype information in the context of translational medicine. In the area of pharmacogenomics and personalized medicine, it has become evident that database applications providing important information on the occurrence and consequences of gene variants involved in pharmacokinetics, pharmacodynamics, drug efficacy and drug toxicity will become an integral tool for researchers and medical practitioners alike. At the same time, two fundamental issues are inextricably linked to current developments, namely data sharing and data protection. Here, we discuss high-throughput and next-generation sequencing technology and its impact on pharmacogenomics research. In addition, we present advances and challenges in the field of pharmacogenomics information systems which have in turn triggered the development of an integrated electronic 'pharmacogenomics assistant'. The system is designed to provide personalized drug recommendations based on linked genotype-to-phenotype pharmacogenomics data, as well as to support biomedical researchers in the identification of pharmacogenomics-related gene variants. The provisioned services are tuned in the framework of a single-access pharmacogenomics portal.

Application of next generation sequencing to CEPH cell lines to discover variants associated with FDA approved chemotherapeutics

BACKGROUND

The goal of this study was to perform candidate gene association with cytotoxicity of chemotherapeutics in cell line models through resequencing and discovery of rare and low frequency variants along with common variations. Here, an association study of cytotoxicity response to 30 FDA approved drugs was conducted and we applied next generation targeted sequencing technology to discover variants from 103 candidate genes in 95 lymphoblastoid cell lines from 14 CEPH pedigrees. In this article, we called variants across 95 cell lines and performed association analysis for cytotoxic response using the Family Based Association Testing method and software.

RESULTS

We called 2281 variable SNP genotypes across the 103 genes for these cell lines and identified three genes of significant association within this marker set. Specifically, ATP-binding cassette, sub-family C, member 5 (ABCC5), metallothionein 1A (MT1A) and NAD(P)H dehydrogenase quinone1 (NQO1) were significantly associated with oxaliplatin drug response. The significant SNP on NQO1 (rs1800566) has been linked with poor survival rates in patients with non-small cell lung cancer treated with cisplatin (which belongs to the same class of drugs as oxaliplatin). A SNP (rs1846692) near the 5' region of MT1A was associated with arsenic trioxide.

CONCLUSIONS

The results from this study are promising and this serves as a proof-of-principle demonstration of the use of sequencing data in the cytotoxicity models of human cell lines. With increased sample sizes, such studies will be a fast and powerful way to associate common and rare variants with drug response; while overcoming the cost and time limitations to recruit cohorts for association study.

TAOK3, a novel genome-wide association study locus associated with morphine requirement and postoperative pain in a retrospective pediatric day surgery population

Candidate gene studies have revealed limited genetic bases for opioid analgesic response variability. Genome-wide association studies facilitate impartial queries of common genetic variants, allowing identification of novel genetic contributions to drug effect. Illumina (Illumina Inc, San Diego, CA, USA) single nucleotide polymorphism (SNP) arrays were used to investigate SNP associations with total morphine requirement as a quantitative trait locus and with postoperative pain in a retrospective population of opioid-naïve children ages 4-18years who had undergone day surgery tonsillectomy and adenoidectomy. In an independent replication cohort, significant genome-wide association studies-identified SNPs were assayed using TaqMan probes. Among 617 comprehensively phenotyped children, the 277 subjects of European Caucasian (EC) ancestry demonstrated nominal association between morphine dose and a series of novel SNPs (top rs795484, P=1.01 × 10(-6) and rs1277441, P=2.77 × 10(-6)) at the TAOK3 locus. Age, body mass index, and physical status were included covariates. Morphine requirement averaged 132.4 μg/kg (SD 40.9). Each minor allele at rs795484 (guanine [G]>adenine [A]) contributed +17.6 μg/kg (95% confidence interval [CI] 10.7-24.4) to dose. Effect direction and magnitude were replicated in an independent cohort of 75 EC children (P<0.05). No association with morphine dose was detected in African Americans (AA) (n=241). Postoperative pain scores ≥ 7/10 were associated with rs795484 (G>A) in the EC cohort (odds ratio 2.35, 95% CI 1.56-3.52, P<0.00005) and this association replicated in AA children (odds ratio 1.76, 95% CI 1.14-2.71, P<0.01). Variants in TAOK3 encoding the serine/threonine-protein kinase, TAO3, are associated with increased morphine requirement in children of EC ancestry and with increased acute postoperative pain in both EC and AA subjects.

Personalized pharmacogenomics: predicting efficacy and adverse drug reactions

Drug response varies between individuals owing to disease heterogeneity, environmental factors, and genetic factors. Genetic factors can affect both the pharmacokinetics and pharmacodynamics of a drug, leading to changes in local and systemic drug exposure and/or changes in the function of the drug target, altering drug response. Several pharmacogenetic biomarkers are already utilized in clinical practice and have been shown to improve clinical outcomes. However, a large number of other biomarkers have never made it beyond the discovery stage. Concerted effort is needed to improve the translation of pharmacogenetic biomarkers into clinical practice, and this will involve the use of standardized phenotyping and genotyping strategies, collaborative work, multidisciplinary approaches to identifying and replicating associations, and cooperation with industry to facilitate translation and commercialization. Acceptance of these approaches by clinicians, regulators, patients, and the public will be important in determining future success.