Databases in the area of pharmacogenetics

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

Development and application of a computable genotype model in the GA4GH Variation Representation Specification

As the diversity of genomic variation data increases with our growing understanding of the role of variation in health and disease, it is critical to develop standards for precise inter-system exchange of these data for research and clinical applications. The Global Alliance for Genomics and Health (GA4GH) Variation Representation Specification (VRS) meets this need through a technical terminology and information model for disambiguating and concisely representing variation concepts. Here we discuss the recent Genotype model in VRS, which may be used to represent the allelic composition of a genetic locus. We demonstrate the use of the Genotype model and the constituent Haplotype model for the precise and interoperable representation of pharmacogenomic diplotypes, HGVS variants, and VCF records using VRS and discuss how this can be leveraged to enable interoperable exchange and search operations between assayed variation and genomic knowledgebases.

Joint Analysis of Phenotypic and Genomic Diversity Sheds Light on the Evolution of Xenobiotic Metabolism in Humans

Variation in genes involved in the absorption, distribution, metabolism, and excretion of drugs (ADME) can influence individual response to a therapeutic treatment. The study of ADME genetic diversity in human populations has led to evolutionary hypotheses of adaptation to distinct chemical environments. Population differentiation in measured drug metabolism phenotypes is, however, scarcely documented, often indirectly estimated via genotype-predicted phenotypes. We administered seven probe compounds devised to target six cytochrome P450 enzymes and the P-glycoprotein (P-gp) activity to assess phenotypic variation in four populations along a latitudinal transect spanning over Africa, the Middle East, and Europe (349 healthy Ethiopian, Omani, Greek, and Czech volunteers). We demonstrate significant population differentiation for all phenotypes except the one measuring CYP2D6 activity. Genome-wide association studies (GWAS) evidenced that the variability of phenotypes measuring CYP2B6, CYP2C9, CYP2C19, and CYP2D6 activity was associated with genetic variants linked to the corresponding encoding genes, and additional genes for the latter three. Instead, GWAS did not indicate any association between genetic diversity and the phenotypes measuring CYP1A2, CYP3A4, and P-gp activity. Genome scans of selection highlighted multiple candidate regions, a few of which included ADME genes, but none overlapped with the GWAS candidates. Our results suggest that different mechanisms have been shaping the evolution of these phenotypes, including phenotypic plasticity, and possibly some form of balancing selection. We discuss how these contrasting results highlight the diverse evolutionary trajectories of ADME genes and proteins, consistent with the wide spectrum of both endogenous and exogenous molecules that are their substrates.

Genetic Variation among Pharmacogenes in the Sardinian Population

Pharmacogenetics (PGx) aims to identify the genetic factors that determine inter-individual differences in response to drug treatment maximizing efficacy while decreasing the risk of adverse events. Estimating the prevalence of PGx variants involved in drug response, is a critical preparatory step for large-scale implementation of a personalized medicine program in a target population. Here, we profiled pharmacogenetic variation in fourteen clinically relevant genes in a representative sample set of 1577 unrelated sequenced Sardinians, an ancient island population that accounts for genetic variation in Europe as a whole, and, at the same time is enriched in genetic variants that are very rare elsewhere. To this end, we used PGxPOP, a PGx allele caller based on the guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), to identify the main phenotypes associated with the PGx alleles most represented in Sardinians. We estimated that 99.43% of Sardinian individuals might potentially respond atypically to at least one drug, that on average each individual is expected to have an abnormal response to about 17 drugs, and that for 27 drugs the fraction of the population at risk of atypical responses to therapy is more than 40%. Finally, we identified 174 pharmacogenetic variants for which the minor allele frequency was at least 10% higher among Sardinians as compared to other European populations, a fact that may contribute to substantial interpopulation variability in drug response phenotypes. This study provides baseline information for further large-scale pharmacogenomic investigations in the Sardinian population and underlines the importance of PGx characterization of diverse European populations, such as Sardinians.

Genetic polymorphisms of pharmacogenomic VIP variants in the Lahu population from Yunnan Province

BACKGROUND

Pharmacogenomics has been widely used to study the very important pharmacogenetic (VIP) variants among populations, but information on pharmacogenomics in the Lahu population is limited. The purpose of this study was to determine the differences in the distribution of VIP variants between the Lahu and the other 26 populations.

METHODS

We genotyped 55 VIP variants of 27 genes in the Lahu population from the PharmGKB database. χ² test was used to compare the genotype and allele frequencies between the Lahu and the other 26 populations from the 1000 Genomes Project.

RESULTS

The genotype and allele frequencies of single nucleotide polymorphisms (SNPs) on rs20417 (PTGS2), rs776746 (CYP3A5), rs2115819 (ALOX5), and rs3093105 (CYP4F2) were considerably different in the Lahu population compared with those in the other 26 populations. Besides, based on the PharmGKB database, we identified several VIP variants that may alter the drug metabolism of aspirin (PTGS2), tacrolimus (CYP3A5), montelukast (ALOX5), and vitamin E (CYP4F2).

CONCLUSION

The results show that there are significant differences in the genotype frequency distribution between the Lahu and the other 26 populations. Our study supplements the pharmacogenomics information of the Lahu population and provides a theoretical basis for individualized medicine in Lahu.

Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants

Background

Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools-these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect.

Methods

Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations.

Results

As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as 'benign'. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as 'benign'. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms.

Conclusion

In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.

Prevalence of five pharmacologically most important CYP2C9 and CYP2C19 allelic variants in the population from the Republic of Srpska in Bosnia and Herzegovina

The enzymes of the cytochrome P450 superfamily play a critical role in phase I drug metabolism. Among them, CYP2C9 and CYP2C19 are clinically important, as they can mediate severe toxicity, therapy failure, and increased susceptibility to cancer and other diseases caused by chemicals. The aim of this study was to determine the prevalence of pharmacologically most important allelic variants of the CYP2C9 and CYP2C19 genes in the general population of the Republic of Srpska (Bosnia and Herzegovina) and to compare them with other populations. For this purpose we determined the genotype profile and allele frequency of 216 randomly selected healthy volunteers using real-time polymerase chain reaction (RT-PCR). The prevalence of the CYP2C9 *2 and *3 alleles was 13.6 and 7.4 %, respectively. Based on these frequencies, of the 216 participants four (1.86 %) were predicted to be poor metabolisers, 78 (36.11 %) intermediate, and the remaining 134 (62.03 %) normal metabolisers. Based on the prevalence of CYP2C19 *2 and *17 variants – 16.2 and 20.4 %, respectively – nine (4.17 %) were predicted to be poor, 57 (26.39 %) rapid, and nine (4.17 %) ultra-rapid metabolisers. We found no significant differences in allele frequencies in our population and populations from other European countries. These findings suggest that genetically determined phenotypes of CYP2C9 and CYP2C19 should be taken into consideration to minimise individual risk and improve benefits of drug therapy in the Republic of Srpska.

Genetic variation of pharmacogenomic VIP variants in Zhuang nationality of southern China

Drug gene polymorphisms are strongly associated with disease. Previous studies have shown that the frequency of drug genes varies in different populations. At present, there are no reports about the polymorphism of the drug genome in the Zhuang population in southern China. This study conducted a pharmacogenomics study on the Zhuang population in southern China. Therefore, we conducted genotyping on 105 Zhuang samples, and compared the genotyping results with those of other 11 ethnic groups after statistical analysis. Our results show that, compared with the 11 populations in the HapMap data set, the differences between the CYP2E1 rs2070676 and CYP2D6 rs1065852 of the Zhuang nationality are the largest. This study fills in the blank of the drug genome information of the Zhuang nationality in southern China. The two sites of Rs2070676 (CYP2E1) and rs1065852 (CYP2D6) provide a reliable basis for the prediction of the efficacy of certain drugs. Its main purpose is to provide theoretical basis for safe drug use in the Zhuang region of southern China.

Polypharmacy: a healthcare conundrum with a pharmacogenetic solution

The use of multiple medications is growing at an alarming rate with some reports documenting an average of 12-22 prescriptions being used by individuals ≥50 years of age. The indirect consequences of polypharmacy include exacerbation of drug-drug interactions, adverse drug reactions, increased likelihood of prescribing cascades, chronic dependence, and hospitalizations - all of which have significant health and economic burden. While many practical solutions for reducing polypharmacy have been proposed, they have been met with limited efficacy. This highlights the need for a new systematic approach for fine-tuning dispensing of medications. Pharmacogenetic testing provides an empirical and scientifically rigorous approach for guiding appropriate selection of medicines, with the potential to reduce unnecessary polypharmacy while improving clinical outcomes. The goal of this review article is to provide healthcare providers with an understanding of polypharmacy, its adverse effects on the healthcare system and highlight how pharmacogenetic information can be used to avoid polypharmacy in patients.

Concepts Driving Pharmacogenomics Implementation Into Everyday Healthcare

Pharmacogenomics (PGx) is often promoted as the domain of precision medicine with the greatest potential to readily impact everyday healthcare. Rapid advances in PGx knowledge derived from extensive basic and clinical research along with decreasing costs of laboratory testing have led to an increased interest in PGx and expectations of imminent clinical translation with substantial clinical impact. However, the implementation of PGx into clinical workflows is neither simple nor straightforward, and comprehensive processes and multidisciplinary collaboration are required. Several national and international institutions have pioneered models for implementing clinical PGx, and these initial models have led to a better understanding of unresolved challenges. In this review, we have categorized and explored the most relevant of these challenges to highlight potential gaps and present possible solutions. We describe the ongoing need for basic and clinical research to drive further developments in evidence-based medicine. Integration into daily clinical workflows introduces new challenges requiring innovative solutions; specifically those related to the electronic health record and embedded clinical decision support. We describe advances in PGx testing and result reporting and describe the critical need for increased standardization in these areas across laboratories. We also explore the complexity of the PGx knowledge required for clinical practice and the need for educational strategies to ensure adequate understanding among members of current and future healthcare teams. Finally, we evaluate knowledge obtained from previous implementation efforts and discuss how to best apply these learnings to future projects. Despite these challenges, the future of precision medicine appears promising due to the rapidity of recent advances in the field and current multidisciplinary efforts to effectively translate PGx to everyday clinical practice.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

Contemporary pharmacogenetic assays in view of the PharmGKB database

AIM

Six modern PGx assays were compared with the Pharmacogenomics Knowledge Base (PharmGKB) to determine the proportion of the currently known PGx genotypes that are assessed by these assays.

MATERIALS & METHODS

Investigated assays were 'Ion AmpliSeq Pharmacogenomics', 'iPLEX PGx Pro', 'DMET Plus,' 'PharmcoScan,' 'Living DNA' and '23andMe.'

RESULTS

PharmGKB contains 3474 clinical annotations of which 75, 70 and 45% can be determined by PharmacoScan, Living DNA and 23andMe, respectively. The other assays are designed to test a specific subset of PGx variants.

CONCLUSION

Assaying all known PGx variants would only comprise a minor fraction of the current assays' capacity. Unfortunately, this is not achieved. Moreover, not necessarily the variants with the highest effects or the highest evidence are selected.

Genetic variation in the Estonian population: pharmacogenomics study of adverse drug effects using electronic health records

Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel loss-of-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery.

A critical appraisal of pharmacogenetic inference

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

Genetic polymorphisms of pharmacogenomic VIP variants in the Yi population from China

INTRODUCTION

Drug response and target therapeutic dosage are different among individuals. The variability is largely genetically determined. With the development of pharmacogenetics and pharmacogenomics, widespread research have provided us a wealth of information on drug-related genetic polymorphisms, and the very important pharmacogenetic (VIP) variants have been identified for the major populations around the world whereas less is known regarding minorities in China, including the Yi ethnic group. Our research aims to screen the potential genetic variants in Yi population on pharmacogenomics and provide a theoretical basis for future medication guidance.

MATERIALS AND METHODS

In the present study, 80 VIP variants (selected from the PharmGKB database) were genotyped in 100 unrelated and healthy Yi adults recruited for our research. Through statistical analysis, we made a comparison between the Yi and other 11 populations listed in the HapMap database for significant SNPs detection. Two specific SNPs were subsequently enrolled in an observation on global allele distribution with the frequencies downloaded from ALlele FREquency Database. Moreover, F-statistics (Fst), genetic structure and phylogenetic tree analyses were conducted for determination of genetic similarity between the 12 ethnic groups.

RESULTS

Using the χ2 tests, rs1128503 (ABCB1), rs7294 (VKORC1), rs9934438 (VKORC1), rs1540339 (VDR) and rs689466 (PTGS2) were identified as the significantly different loci for further analysis. The global allele distribution revealed that the allele "A" of rs1540339 and rs9934438 were more frequent in Yi people, which was consistent with the most populations in East Asia. F-statistics (Fst), genetic structure and phylogenetic tree analyses demonstrated that the Yi and CHD shared a closest relationship on their genetic backgrounds. Additionally, Yi was considered similar to the Han people from Shaanxi province among the domestic ethnic populations in China.

CONCLUSIONS

Our results demonstrated significant differences on several polymorphic SNPs and supplement the pharmacogenomic information for the Yi population, which could provide new strategies for optimizing clinical medication in accordance with the genetic determinants of drug toxicity and efficacy.

Natural variation in a single amino acid substitution underlies physiological responses to topoisomerase II poisons

Many chemotherapeutic drugs are differentially effective from one patient to the next. Understanding the causes of this variability is a critical step towards the development of personalized treatments and improvements to existing medications. Here, we investigate sensitivity to a group of anti-neoplastic drugs that target topoisomerase II using the model organism Caenorhabditis elegans. We show that wild strains of C. elegans vary in their sensitivity to these drugs, and we use an unbiased genetic approach to demonstrate that this natural variation is explained by a methionine-to-glutamine substitution in topoisomerase II (TOP-2). The presence of a non-polar methionine at this residue increases hydrophobic interactions between TOP-2 and its poison etoposide, as compared to a polar glutamine. We hypothesize that this stabilizing interaction results in increased genomic instability in strains that contain a methionine residue. The residue affected by this substitution is conserved from yeast to humans and is one of the few differences between the two human topoisomerase II isoforms (methionine in hTOPIIα and glutamine in hTOPIIβ). We go on to show that this amino acid difference between the two human topoisomerase isoforms influences cytotoxicity of topoisomerase II poisons in human cell lines. These results explain why hTOPIIα and hTOPIIβ are differentially affected by various poisons and demonstrate the utility of C. elegans in understanding the genetics of drug responses.

The severe cytotoxic effects associated with anti-neoplastic treatment regimens make it difficult to assess the contributions of genetic variation on treatment responses in clinical settings. Therefore, we leveraged genetic diversity present in the metazoan model nematode Caenorhabditis elegans to identify genetic variants that contribute to differential susceptibility to a broadly administered class of anti-neoplastic compounds that poison the activity of topoisomerase II enzymes. We show that wild C. elegans isolates contain either glutamine or methionine at a highly conserved residue of the topoisomerase II (TOP-2) protein and that this substitution is predictive of animal responses to the topoisomerase II poisons etoposide, teniposide, dactinomycin, and XK469. Interestingly, the two human versions of this protein, hTOPIIα and hTOPIIβ, contain a methionine or glutamine at the corresponding residue, respectively. We show that this difference between the two human topoisomerase II isoforms contributes to the differential cytotoxicity induced by these drugs. Taken together, our results highlight the power of studying the effects of natural genetic variation on drug responses in a model organism and propose methods to develop new drugs that have increased affinity for the desired hTOPIIα isoform expressed in tumor cells.

Methods to analyze big data in pharmacogenomics research

The scale and scope of pharmacogenomics research continues to expand as the cost and efficiency of molecular data generation techniques advance. These new technologies give rise to enormous opportunity for the identification of important genetic and genomic factors important for drug treatment response. With this opportunity come significant challenges. Most of these can be categorized as 'big data' issues, facing not only pharmacogenomics, but other fields in the life sciences as well. In this review, we describe some of the analysis techniques and tools being implemented for genetic/genomic discovery in pharmacogenomics.

ePGA: A Web-Based Information System for Translational Pharmacogenomics

One of the challenges that arise from the advent of personal genomics services is to efficiently couple individual data with state of the art Pharmacogenomics (PGx) knowledge. Existing services are limited to either providing static views of PGx variants or applying a simplistic match between individual genotypes and existing PGx variants. Moreover, there is a considerable amount of haplotype variation associated with drug metabolism that is currently insufficiently addressed. Here, we present a web-based electronic Pharmacogenomics Assistant (ePGA; http://www.epga.gr/) that provides personalized genotype-to-phenotype translation, linked to state of the art clinical guidelines. ePGA's translation service matches individual genotype-profiles with PGx gene haplotypes and infers the corresponding diplotype and phenotype profiles, accompanied with summary statistics. Additional features include i) the ability to customize translation based on subsets of variants of clinical interest, and ii) to update the knowledge base with novel PGx findings. We demonstrate ePGA's functionality on genetic variation data from the 1000 Genomes Project.

Genetic polymorphisms study of pharmacogenomic VIP variants in Han ethnic of China's Shaanxi province

BACKGROUND

Multiple factors include genetic and non-genetic interactions induce to different drug response among different individuals. Lots of researches proved that different frequencies of genetic variants exists different ethnic groups. The aim of this study was to screen Han volunteers in Shaanxi for VIP gene polymorphisms.

MATERIALS AND METHODS

We genotyped 80 Very Important Pharmacogenes (VIP) (selected from the PharmGKB database) in 192 unrelated, healthy Han ethnic adults from Shaanxi, the northwest of China, and then analyzed genotyping data wtih Structure and F-statistics (Fst) analysis.

RESULTS

We compared our data with 15 other populations (Deng, Kyrgyz, Tajik, Uygur and 11 HapMap populations), and found the frequency distribution of Han population in Shaanxi is most similar with CHB. Also, Structure and Fst showed that Shaanxi Han has a closest genetic background with CHB.

CONCLUSIONS

Our study have supplemented the Han Chinese data related to pharmacogenomics and illustrated differences in genotypic frequencies of selected VIP variants' among the Han population and 15 other populations.

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.

Genetic polymorphism of pharmacogenomic VIP variants in the Deng people from the Himalayas in Southeast Tibet

Little is known about polymorphic distribution of pharmacogenes among ethnicities, including the Deng people. In this study, we recruited 100 unrelated, healthy Deng people and genotyped them with respect to 76 different single-nucleotide polymorphisms by the PharmGKB database. Our results first indicated that the polymorphic distribution of pharmacogenes of the Deng people is most similar to CHD, suggesting that Deng people have a closest genetic relationship with CHD. Our data will enrich the database of pharmacogenomics and provide a theoretical basis for safer drug administration and individualized treatment plans, promoting the development of personalized medicine.

Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease

The potential for personalized sequencing to individually optimize medical treatment in diseases such as cancer and for pharmacogenomic application is just beginning to be realized, and the utility of sequencing healthy individuals for managing health is also being explored. The data produced requires additional advancements in interpretation of variants of unknown significance to maximize clinical benefit. Nevertheless, personalized sequencing, only recently applied to clinical medicine, has already been broadly applied to the discovery and study of disease. It is poised to enable the earlier and more accurate diagnosis of disease risk and occurrence, guide prevention and individualized intervention as well as facilitate monitoring of healthy and treated patients, and play a role in the prevention and recurrence of future disease. This article documents the advancing capacity of personalized sequencing, reviews its impact on disease-oriented scientific discovery and anticipates its role in the future of medicine.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

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.

PharmGKB: the Pharmacogenomics Knowledge Base

The Pharmacogenomics Knowledge Base, PharmGKB, is an interactive tool for researchers investigating how genetic variation affects drug response. The PharmGKB Web site, http://www.pharmgkb.org , displays genotype, molecular, and clinical knowledge integrated into pathway representations and Very Important Pharmacogene (VIP) summaries with links to additional external resources. Users can search and browse the knowledgebase by genes, variants, drugs, diseases, and pathways. Registration is free to the entire research community, but subject to agreement to use for research purposes only and not to redistribute. Registered users can access and download data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

Complexities of CYP2D6 gene analysis and interpretation

Cytochrome P450 2D6 (CYP2D6) plays an important role in the metabolism and bioactivation of about 25% of clinically used drugs including many antidepressants, antipsychotics and opioids. CYP2D6 activity is highly variably ranging from no activity in so-called poor metabolizers to ultrarapid metabolism at the other end of the extreme of the activity distribution. A large portion of this variability can be explained by the highly polymorphic nature of the CYP2D6 gene locus for which > 100 variants and subvariants identified to date. Allele frequencies vary markedly between ethnic groups; some have exclusively or predominantly only been observed in certain populations. Pharmacogenetic testing holds the promise of individualizing drug therapy by identifying patients with CYP2D6 diplotypes that puts them at an increased risk of experiencing dose-related adverse events or therapeutic failure. Inferring a patient's CYP2D6 metabolic capacity, or phenotype, however, is a challenging task due to the complexity of the CYP2D6 gene locus. Allelic variation includes SNPs, small insertions and deletions, gene copy number variation and rearrangements with CYP2D7, a highly related non-functional gene. This review provides a summary of the intricacies of CYP2D6 variation and genotype analysis, knowledge that is invaluable for the translation of genotype into clinically useful information.

Approaches for dosage individualisation in critically ill patients

INTRODUCTION

Pharmacokinetic variability in critically ill patients is the result of the overlapping of multiple pathophysiological and clinical factors. Unpredictable exposure from standard dosage regimens may influence the outcome of treatment. Therefore, strategies for dosage individualisation are recommended in this setting.

AREAS COVERED

The authors focus on several approaches for dosage individualisation that have been developed, ranging from the well-established therapeutic drug monitoring (TDM) up to the innovative application of pharmacogenomics criteria. Furthermore, the authors summarise the specific population pharmacokinetic models for different drugs developed for critically ill patients to improve the initial dosage selection and the Bayesian forecasting of serum concentrations. The authors also consider the use of Monte Carlo simulation for the selection of dosage strategies.

EXPERT OPINION

Pharmacokinetic/pharmacodynamics (PK/PD) modelling and dosage individualisation methods based on mathematical and statistical criteria will contribute in improving pharmacologic treatment in critically ill patients. Moreover, substantial effort will be necessary to integrate pharmacogenomics criteria into critical care practice. The lack of availability of target biomarkers for dosage adjustment emphasizes the value of TDM which allows a large part of treatment outcome variability to be controlled.

Update on allele nomenclature for human cytochromes P450 and the Human Cytochrome P450 Allele (CYP-allele) Nomenclature Database

Interindividual variability in xenobiotic metabolism and drug response is extensive and genetic factors play an important role in this variation. A majority of clinically used drugs are substrates for the cytochrome P450 (CYP) enzyme system and interindividual variability in expression and function of these enzymes is a major factor for explaining individual susceptibility for adverse drug reactions and drug response. Because of the existence of many polymorphic CYP genes, for many of which the number of allelic variants is continually increasing, a universal and official nomenclature system is important. Since 1999, all functionally relevant polymorphic CYP alleles are named and published on the Human Cytochrome P450 Allele (CYP-allele) Nomenclature Web site (http://www.cypalleles.ki.se). Currently, the database covers nomenclature of more than 660 alleles in a total of 30 genes that includes 29 CYPs as well as the cytochrome P450 oxidoreductase (POR) gene. On the CYP-allele Web site, each gene has its own Webpage, which lists the alleles with their nucleotide changes, their functional consequences, and links to publications identifying or characterizing the alleles. CYP2D6, CYP2C9, CYP2C19, and CYP3A4 are the most important CYPs in terms of drug metabolism, which is also reflected in their corresponding highest number of Webpage hits at the CYP-allele Web site.The main advantage of the CYP-allele database is that it offers a rapid online publication of CYP-alleles and their effects and provides an overview of peer-reviewed data to the scientific community. Here, we provide an update of the CYP-allele database and the associated nomenclature.

Pharmacogenomics of chemotherapeutic susceptibility and toxicity

The goal of personalized medicine is to tailor a patient's treatment strategy on the basis of his or her unique genetic make-up. The field of oncology is beginning to incorporate many of the strategies of personalized medicine, especially within the realm of pharmacogenomics, which is the study of how inter-individual genetic variation determines drug response or toxicity. A main objective of pharmacogenomics is to facilitate physician decision-making regarding optimal drug selection, dose and treatment duration on a patient-by-patient basis. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. This review focuses on the contribution of germline genetic variation to chemotherapeutic toxicity and response, and discusses the utility of genome-wide association studies and use of lymphoblastoid cell lines (LCLs) in pharmacogenomic studies. Furthermore, we highlight several recent examples of genetic variants associated with chemotherapeutic toxicity or response in both patient cohorts and LCLs, and discuss the challenges and future directions of pharmacogenomic discovery for cancer treatment.

Using companion and coupled diagnostics within strategy to personalize targeted medicines

Regulatory authorities expect the pharmaceutical and biotechnology industries to accelerate their development of companion diagnostics and companion therapeutics towards the goal of personalized medicine, and expect health services to fund, prescribers to adopt and patients to accept these new therapeutic technologies. Expected benefits from more systematic development of combination products (companion diagnostic and its companion therapeutic) are expected to include safer and improved clinical and cost-effective use of medicines, more efficient patient selection for clinical trials, more cost-effective treatment pathways for health services, and a more profitable approach for drug developers. This review discusses challenges to timely development of companion diagnostics and provides case studies of single and multiple protein and genetic biomarkers of clinical response and risk of adverse drug effects.

Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics

MOTIVATION

Many complex diseases are the result of abnormal pathway functions instead of single abnormalities. Disease diagnosis and intervention strategies must target these pathways while minimizing the interference with normal physiological processes. Large-scale identification of disease pathways and chemicals that may be used to perturb them requires the integration of information about drugs, genes, diseases and pathways. This information is currently distributed over several pharmacogenomics databases. An integrated analysis of the information in these databases can reveal disease pathways and facilitate novel biomedical analyses.

RESULTS

We demonstrate how to integrate pharmacogenomics databases through integration of the biomedical ontologies that are used as meta-data in these databases. The additional background knowledge in these ontologies can then be used to enable novel analyses. We identify disease pathways using a novel multi-ontology enrichment analysis over the Human Disease Ontology, and we identify significant associations between chemicals and pathways using an enrichment analysis over a chemical ontology. The drug-pathway and disease-pathway associations are a valuable resource for research in disease and drug mechanisms and can be used to improve computational drug repurposing.

AVAILABILITY

http://pharmgkb-owl.googlecode.com

Single nucleotide polymorphisms as susceptibility, prognostic, and therapeutic markers of nonsmall cell lung cancer

Lung cancer is a major public health problem throughout the world. Among the most frequent cancer types (prostate, breast, colorectal, stomach, lung), lung cancer is the leading cause of cancer-related deaths worldwide. Among the two major subtypes of small cell lung cancer and nonsmall cell lung cancer (NSCLC), 85% of tumors belong to the NSCLC histological types. Small cell lung cancer is associated with the shortest survival time. Although tobacco smoking has been recognized as the major risk factor for lung cancer, there is a great interindividual and interethnic difference in risk of developing lung cancer given exposure to similar environmental and lifestyle factors. This may indicate that in addition to chemical and environmental factors, genetic variations in the genome may contribute to risk modification. A common type of genetic variation in the genome, known as single nucleotide polymorphism, has been found to be associated with susceptibility to lung cancer. Interestingly, many of these polymorphisms are found in the genes that regulate major pathways of carcinogen metabolism (cytochrome P450 genes), detoxification (glutathione S-transferases), adduct removal (DNA repair genes), cell growth/apoptosis (TP53/MDM2), the immune system (cytokines/chemokines), and membrane receptors (nicotinic acetylcholine and dopaminergic receptors). Some of these polymorphisms have been shown to alter the level of mRNA, and protein structure and function. In addition to being susceptibility markers, several of these polymorphisms are emerging to be important for response to chemotherapy/radiotherapy and survival of patients. Therefore, it is hypothesized that single nucleotide polymorphisms will be valuable genetic markers in individual-based prognosis and therapy in future. Here we will review some of the most important single nucleotide polymorphisms in the metabolic pathways that may modulate susceptibility, prognosis, and therapy in NSCLC.

Bioinformatics for human genetics: promises and challenges

Recent developments, including next-generation sequencing (NGS), bio-ontologies and the Semantic Web, and the growing role of hospital information technology (IT) systems and electronic health records, amass ever-increasing amounts of data before human genetics scientists and clinicians. However, they have ever-improving tools to analyze those data for research and clinical care. Correspondingly, the field of bioinformatics is turning to research questions in the field of human genetics, and the field of human genetics is making greater use of bioinformatic algorithms and tools. The choice of "Bioinformatics and Human Genetics" as the topic of this special issue of Human Mutation reflects this new importance of bioinformatics and medical informatics in human genetics. Experts from among the attendees of the Paris 2010 Human Variome Project symposium provide a survey of some of the "hot" computational topics over the next decade. These experts identify the promise-what human geneticists who are not themselves bioinformaticians stand to gain-as well as the challenges and unmet needs that are likely to represent fruitful areas of research.