Genome-wide association studies in pharmacogenomics

Targeted next-generation sequencing panel to investigate antiplatelet adverse reactions in acute coronary syndrome patients undergoing percutaneous coronary intervention with stenting

Antiplatelet therapy, the gold standard of care for patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI), is one of the therapeutic approaches most associated with the development of adverse drug reactions (ADRs). Although numerous studies have shown that pharmacological intervention based on a limited number of high-evidence variants (primarily CYP2C19*2 and *3) can reduce the incidence of major adverse cardiovascular events (MACEs), ADRs still occur at variable rates (10.1 % in our case) despite personalized therapy. This study aimed to identify novel genetic variants associated with the endpoint of MACEs 12 months after PCI by designing and analyzing a targeted gene panel. We sequenced 244 ACS-PCI-stent patients (109 with event and 135 without event) and 99 controls without structural cardiovascular disease and performed an association analysis to search for unexpected genetic variants. No single nucleotide polymorphisms reached genomic significance after correction, but three novel variants, including ABCA1 (rs2472434), KLB (rs17618244), and ZNF335 (rs3827066), may play a role in MACEs in ACS patients. These genetic variants are involved in regulating high-density lipoprotein levels and cholesterol deposition, and as they are regulatory variants, they may affect the expression of nearby lipid metabolism-related genes. Our findings suggest new targets (both at the gene and pathway levels) that may increase susceptibility to MACEs, but further research is needed to clarify the role and impact of the identified variants before these findings can be incorporated into the therapeutic decision-making process.

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles

Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.

Causal inference in drug discovery and development

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Cytochrome P450 Transcriptional Regulation by Testis-Specific Y-Encoded-Like Protein: Identification of Novel Upstream Transcription Factors

Cytochrome P450s (CYPs) display significant inter-individual variation in expression, much of which remains unexplained by known CYP single-nucleotide polymorphisms (SNPs). Testis-specific Y-encoded-like proteins (TSPYLs) are transcriptional regulators for several drug-metabolizing CYPs including CYP3A4 However, transcription factors (TFs) that might influence CYP expression through an effect on TSPYL expression are unknown. Therefore, we studied regulators of TSPYL expression in hepatic cell lines and their possible SNP-dependent variation. Specifically, we identified candidate TFs that might influence TSPYL expression using the ENCODE ChIPseq database. Subsequently, the expression of TSPYL1/2/4 as well as that of selected CYP targets for TSPYL regulation were assayed in hepatic cell lines before and after knockdown of TFs that might influence CYP expression through TSPYL-dependent mechanisms. Those results were confirmed by studies of TF binding to TSPYL1/2/4 gene promoter regions. In hepatic cell lines, knockdown of the REST and ZBTB7A TFs resulted in decreased TSPYL1 and TSPYL4 expression and increased CYP3A4 expression, changes reversed by TSPYL1/4 overexpression. Potential binding sites for REST and ZBTB7A on the promoters of TSPYL1 and TSPYL4 were confirmed by chromatin immunoprecipitation. Finally, common SNP variants in upstream binding sites on the TSPYL1/4 promoters were identified and luciferase reporter constructs confirmed SNP-dependent modulation of TSPYL1/4 gene transcription. In summary, we identified REST and ZBTB7A as regulators of the expression of TSPYL genes which themselves can contribute to regulation of CYP expression and-potentially-of drug metabolism. SNP-dependent modulation of TSPYL transcription may contribute to individual variation in both CYP expression and-downstream-drug response phenotypes. SIGNIFICANCE STATEMENT: Testis-specific Y-encoded-like proteins (TSPYLs) are transcriptional regulators of cytochrome P450 (CYP) gene expression. Here, we report that variation in TSPYL expression as a result of the effects of genetically regulated TSPYL transcription factors is an additional factor that could result in downstream variation in CYP expression and potentially, as a result, variation in drug biotransformation.

Genome-Wide Association Study Identifies Candidate Loci Associated with Opioid Analgesic Requirements in the Treatment of Cancer Pain

Considerable individual differences have been widely observed in the sensitivity to opioids. We conducted a genome-wide association study (GWAS) in patients with cancer pain to identify potential candidate single-nucleotide polymorphisms (SNPs) that contribute to individual differences in opioid analgesic requirements in pain treatment by utilizing whole-genome genotyping arrays with more than 650,000 markers. The subjects in the GWAS were 428 patients who provided written informed consent and underwent treatment for pain with opioid analgesics in a palliative care unit at Higashi-Sapporo Hospital. The GWAS showed two intronic SNPs, rs1283671 and rs1283720, in the ANGPT1 gene that encodes a secreted glycoprotein that belongs to the angiopoietin family. These two SNPs were strongly associated with average daily opioid requirements for the treatment of pain in both the additive and recessive models (p < 5.0000 × 10−8). Several other SNPs were also significantly associated with the phenotype. In the gene-based analysis, the association was significant for the SLC2A14 gene in the additive model. These results indicate that these SNPs could serve as markers that predict the efficacy of opioid analgesics in cancer pain treatment. Our findings may provide valuable information for achieving satisfactory pain control and open new avenues for personalized pain treatment.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Genome-Wide Studies in Ischaemic Stroke: Are Genetics Only Useful for Finding Genes?

Ischaemic stroke is a complex disease with some degree of heritability. This means that heritability factors, such as genetics, could be risk factors for ischaemic stroke. The era of genome-wide studies has revealed some of these heritable risk factors, although the data generated by these studies may also be useful in other disciplines. Analysis of these data can be used to understand the biological mechanisms associated with stroke risk and stroke outcome, to determine the causality between stroke and other diseases without the need for expensive clinical trials, or to find potential drug targets with higher success rates than other strategies. In this review we will discuss several of the most relevant studies regarding the genetics of ischaemic stroke and the potential use of the data generated.

Different responses to risperidone treatment in Schizophrenia: a multicenter genome-wide association and whole exome sequencing joint study

Risperidone is routinely used in the clinical management of schizophrenia, but the treatment response is highly variable among different patients. The genetic underpinnings of the treatment response are not well understood. We performed a pharmacogenomic study of the treatment response to risperidone in patients with schizophrenia by using a SNP microarray -based genome-wide association study (GWAS) and whole exome sequencing (WES)-based GWAS. DNA samples were collected from 189 patients for the GWAS and from 222 patients for the WES after quality control in multiple centers of China. Antipsychotic response phenotypes of patients who received eight weeks of risperidone treatment were quantified with percentage change on the Positive and Negative Syndrome Scale (PANSS). The GWAS revealed a significant association between several SNPs and treatment response, such as three GRM7 SNPs (rs141134664, rs57521140, and rs73809055). Gene-based analysis in WES revealed 13 genes that were associated with antipsychotic response, such as GPR12 and MAP2K3. We did not identify shared loci or genes between GWAS and WES, but association signals tended to cluster into the GPCR gene family and GPCR signaling pathway, which may play an important role in the treatment response etiology. This study may provide a research paradigm for pharmacogenomic research, and these data provide a promising illustration of our potential to identify genetic variants underlying antipsychotic responses and may ultimately facilitate precision medicine in schizophrenia.

Associations between the C3orf20 rs12496846 Polymorphism and Both Postoperative Analgesia after Orthognathic and Abdominal Surgeries and C3orf20 Gene Expression in the Brain

Considerable individual differences are widely observed in the sensitivity to opioid analgesics. We focused on rs12496846, rs698705, and rs10052295 single-nucleotide polymorphisms (SNPs) in the C3orf20, SLC8A2, and CTNND2 gene regions that we previously identified as possibly associated with postoperative analgesia after orthognathic surgery. We investigated associations between these SNPs and postoperative analgesia in 112 patients who underwent major open abdominal surgery in hospitals and were treated with analgesics, including opioids, after surgery. Total genomic DNA was extracted from peripheral blood or oral mucosa samples for genotyping each SNP. Effects of these potent SNPs on gene expression in the brain were also investigated in samples that were provided by the Stanley Foundation Brain Bank. In the association studies, carriers of the G allele of the rs12496846 SNP in the C3orf20 gene region were significantly associated with greater 24 h postoperative analgesic requirements among the three SNPs that were investigated (p = 0.0015), which corroborated a previous study of orthognathic patients (p < 0.0001). In the gene expression analysis, carriers of the G allele of the rs12496846 SNP were significantly associated with lower mRNA expression of the C3orf20 gene (p < 0.0001). These results indicate that this SNP could serve as a marker that predicts analgesic requirements.

From Pharmacogenetics to Pharmaco-Omics:Milestones and Future Directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

Genetic Diversity in Chimpanzee Transcriptomics Does Not Represent Wild Populations

Chimpanzees (Pan troglodytes) are a genetically diverse species, consisting of four highly distinct subspecies. As humans' closest living relative, they have been a key model organism in the study of human evolution, and comparisons of human and chimpanzee transcriptomes have been widely used to characterize differences in gene expression levels that could underlie the phenotypic differences between the two species. However, the subspecies from which these transcriptomic data sets have been derived is not recorded in metadata available in the public NCBI Sequence Read Archive (SRA). Furthermore, labeling of RNA sequencing (RNA-seq) samples is for the most part inconsistent across studies, and the true number of individuals from whom transcriptomic data are available is difficult to ascertain. Thus, we have evaluated genetic diversity at the subspecies and individual level in 486 public RNA-seq samples available in the SRA, spanning the vast majority of public chimpanzee transcriptomic data. Using multiple population genetics approaches, we find that nearly all samples (96.6%) have some degree of Western chimpanzee ancestry. At the individual donor level, we identify multiple samples that have been repeatedly analyzed across different studies and identify a total of 135 genetically distinct individuals within our data, a number that falls to 89 when we exclude likely first- and second-degree relatives. Altogether, our results show that current transcriptomic data from chimpanzees are capturing low levels of genetic diversity relative to what exists in wild chimpanzee populations. These findings provide important context to current comparative transcriptomics research involving chimpanzees.

Towards precision medicine in heart failure

The number of therapies for heart failure (HF) with reduced ejection fraction has nearly doubled in the past decade. In addition, new therapies for HF caused by hypertrophic and infiltrative disease are emerging rapidly. Indeed, we are on the verge of a new era in HF in which insights into the biology of myocardial disease can be matched to an understanding of the genetic predisposition in an individual patient to inform precision approaches to therapy. In this Review, we summarize the biology of HF, emphasizing the causal relationships between genetic contributors and traditional structure-based remodelling outcomes, and highlight the mechanisms of action of traditional and novel therapeutics. We discuss the latest advances in our understanding of both the Mendelian genetics of cardiomyopathy and the complex genetics of the clinical syndrome presenting as HF. In the phenotypic domain, we discuss applications of machine learning for the subcategorization of HF in ways that might inform rational prescribing of medications. We aim to bridge the gap between the biology of the failing heart, its diverse clinical presentations and the range of medications that we can now use to treat it. We present a roadmap for the future of precision medicine in HF.

The need to shift pharmacogenetic research from candidate gene to genome-wide association studies

The primary research approach in pharmacogenetics has been candidate gene association studies (CGAS), but pharmacogenomic genome-wide association studies (GWAS) are becoming more common. We are now at a critical juncture when the results of those two research approaches, CGAS and GWAS, can be compared in pharmacogenetics. We analyzed publicly available databases of pharmacogenetic CGAS and GWAS (i.e., the Pharmacogenomics Knowledgebase [PharmGKB^®] and the NHGRI-EBI GWAS catalog) and the vast majority of variants (98%) and genes (94%) discovered in pharmacogenomic GWAS were novel (i.e., not previously studied CGAS). Therefore, pharmacogenetic researchers are not selecting the right candidate genes in the vast majority of CGAS, highlighting a need to shift pharmacogenetic research efforts from CGAS to GWAS.

Pharmacometabolomics by NMR in Oncology: A Systematic Review

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

Current opinion on the pharmacogenomics of paclitaxel-induced toxicity

Introduction: Paclitaxel is a microtubule stabilizer that is currently one of the most utilized chemotherapeutic agents. Its efficacy in breast, uterine, lung and other neoplasms made its safety profile enhancement a subject of great interest. Neurotoxicity is the most common paclitaxel-associated toxicities. In addition, hypersensitivity reactions, hematological, gastrointestinal, and cardiac toxicities are all encountered.Areas covered: The current review explores paclitaxel-induced toxicities mechanisms and risk factors. Studies investigating these toxicities pharmacogenomic biomarkers are reviewed and summarized. There is a limited margin of consistency between the retrieved associations. Variants in genes related to neuro-sensitivity are the most promising candidates for future studies.Expert opinion: Genome-wide association studies highlighted multiple-candidate biomarkers relevant to neuro-sensitivity. Most of the identified paclitaxel-neurotoxicity candidate genes are derived from congenital neuropathy and diabetic-induced neurotoxicity pathways. Future studies should explore these sets of genes while considering the multifactorial nature of paclitaxel-induced neurotoxicity. In the absence of certain paclitaxel-toxicity biomarkers, future research should avoid earlier studies' caveats. Genes in paclitaxel's pharmacokinetic pathways could not provide consistent results in any of its associated toxicities. There is a need to dig deeper into toxicity-development mechanisms and personal vulnerability factors, rather than targeting only the genes suspected to affect drug exposure.

Pharmacogenomics in the era of next generation sequencing - from byte to bedside

Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.

Stem Cells for Next Level Toxicity Testing in the 21st Century

The call for a paradigm change in toxicology from the United States National Research Council in 2007 initiates awareness for the invention and use of human-relevant alternative methods for toxicological hazard assessment. Simple 2D in vitro systems may serve as first screening tools, however, recent developments infer the need for more complex, multicellular organotypic models, which are superior in mimicking the complexity of human organs. In this review article most critical organs for toxicity assessment, i.e., skin, brain, thyroid system, lung, heart, liver, kidney, and intestine are discussed with regards to their functions in health and disease. Embracing the manifold modes-of-action how xenobiotic compounds can interfere with physiological organ functions and cause toxicity, the need for translation of such multifaceted organ features into the dish seems obvious. Currently used in vitro methods for toxicological applications and ongoing developments not yet arrived in toxicity testing are discussed, especially highlighting the potential of models based on embryonic stem cells and induced pluripotent stem cells of human origin. Finally, the application of innovative technologies like organs-on-a-chip and genome editing point toward a toxicological paradigm change moves into action.

Efficient verification for outsourced genome-wide association studies

With cloud computing is being widely adopted in conducting genome-wide association studies (GWAS), how to verify the integrity of outsourced GWAS computation remains to be accomplished. Here, we propose two novel algorithms to generate synthetic SNPs that are indistinguishable from real SNPs. The first method creates synthetic SNPs based on the phenotype vector, while the second approach creates synthetic SNPs based on real SNPs that are most similar to the phenotype vector. The time complexity of the first approach and the second approach is Om and Omlogn², respectively, where m is the number of subjects while n is the number of SNPs. Furthermore, through a game theoretic analysis, we demonstrate that it is possible to incentivize honest behavior by the server by coupling appropriate payoffs with randomized verification. We conduct extensive experiments of our proposed methods, and the results show that beyond a formal adversarial model, when only a few synthetic SNPs are generated and mixed into the real data they cannot be distinguished from the real SNPs even by a variety of predictive machine learning models. We demonstrate that the proposed approach can ensure that logistic regression for GWAS can be outsourced in an efficient and trustworthy way.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Genome-Wide Association Study of Metamizole-Induced Agranulocytosis in European Populations

Agranulocytosis is a rare yet severe idiosyncratic adverse drug reaction to metamizole, an analgesic widely used in countries such as Switzerland and Germany. Notably, an underlying mechanism has not yet been fully elucidated and no predictive factors are known to identify at-risk patients. With the aim to identify genetic susceptibility variants to metamizole-induced agranulocytosis (MIA) and neutropenia (MIN), we conducted a retrospective multi-center collaboration including cases and controls from three European populations. Association analyses were performed using genome-wide genotyping data from a Swiss cohort (45 cases, 191 controls) followed by replication in two independent European cohorts (41 cases, 273 controls) and a joint discovery meta-analysis. No genome-wide significant associations (p < 1 × 10⁻⁷) were observed in the Swiss cohort or in the joint meta-analysis, and no candidate genes suggesting an immune-mediated mechanism were identified. In the joint meta-analysis of MIA cases across all cohorts, two candidate loci on chromosome 9 were identified, rs55898176 (OR = 4.01, 95%CI: 2.41–6.68, p = 1.01 × 10⁻⁷) and rs4427239 (OR = 5.47, 95%CI: 2.81–10.65, p = 5.75 × 10⁻⁷), of which the latter is located in the SVEP1 gene previously implicated in hematopoiesis. This first genome-wide association study for MIA identified suggestive associations with biological plausibility that may be used as a stepping-stone for post-GWAS analyses to gain further insight into the mechanism underlying MIA.

MASS SPECTROMETRY-BASED PERSONALIZED DRUG THERAPY

Personalized drug therapy aims to provide tailored treatment for individual patient. Mass spectrometry (MS) is revolutionarily involved in this area because MS is a rapid, customizable, cost-effective, and easy to be used high-throughput method with high sensitivity, specificity, and accuracy. It is driving the formation of a new field, MS-based personalized drug therapy, which currently mainly includes five subfields: therapeutic drug monitoring (TDM), pharmacogenomics (PGx), pharmacomicrobiomics, pharmacoepigenomics, and immunopeptidomics. Gas chromatography-MS (GC-MS) and liquid chromatography-MS (LC-MS) are considered as the gold standard for TDM, which can be used to optimize drug dosage. Matrix-assisted laser desorption ionization-time of flight-MS (MALDI-TOF-MS) significantly improves the capability of detecting biomacromolecule, and largely promotes the application of MS in PGx. It is becoming an indispensable tool for genotyping, which is used to discover and validate genetic biomarkers. In addition, MALDI-TOF-MS also plays important roles in identity of human microbiome whose diversity can explain interindividual differences of drug response. Pharmacoepigenetics is to study the role of epigenetic factors in individualized drug treatment. MS can be used to discover and validate pharmacoepigenetic markers (DNA methylation, histone modification, and noncoding RNA). For the emerging cancer immunotherapy, personalized cancer vaccine has effective immunotherapeutic activity in the clinic. MS-based immunopeptidomics can effectively discover and screen neoantigens. This article systematically reviewed MS-based personalized drug therapy in the above mentioned five subfields. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Variant discovery using next-generation sequencing and its future role in pharmacogenetics

Next-generation sequencing (NGS) has enabled the discovery of a multitude of novel and mostly rare variants in pharmacogenes that may alter a patient’s therapeutic response to drugs. In addition to single nucleotide variants, structural variation affecting the number of copies of whole genes or parts of genes can be detected. While current guidelines concerning clinical implementation mostly act upon well-documented, common single nucleotide variants to guide dosing or drug selection, in silico and large-scale functional assessment of rare variant effects on protein function are at the forefront of pharmacogenetic research to facilitate their clinical integration. Here, we discuss the role of NGS in variant discovery, paving the way for more comprehensive genotype-guided pharmacotherapy that can translate to improved clinical care.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Heart Failure in the Era of Precision Medicine: A Scientific Statement From the American Heart Association

One of 5 people will develop heart failure over his or her lifetime. Early diagnosis and better understanding of the pathophysiology of this disease are critical to optimal treatment. The “omics”—genomics, pharmacogenomics, epigenomics, proteomics, metabolomics, and microbiomics— of heart failure represent rapidly expanding fields of science that have, to date, not been integrated into a single body of work. The goals of this statement are to provide a comprehensive overview of the current state of these omics as they relate to the development and progression of heart failure and to consider the current and potential future applications of these data for precision medicine with respect to prevention, diagnosis, and therapy.

New insights into tardive dyskinesia genetics: Implementation of whole-exome sequencing approach

Tardive dyskinesia (TD) is an adverse movement disorder induced by chronic treatment with antipsychotics drugs. The contribution of common genetic variants to TD susceptibility has been investigated in recent years, but with limited success. The aim of the current study was to investigate the potential contribution of rare variants to TD vulnerability. In order to identify TD risk genes, we performed whole-exome sequencing (WES) and gene-based collapsing analysis focusing on rare (allele frequency < 1%) and putatively deleterious variants (qualifying variants). 82 Jewish schizophrenia patients chronically treated with antipsychotics were included and classified as having severe TD or lack of any abnormal movements based on a rigorous definition of the TD phenotype. First, we performed a case-control, exome-wide collapsing analysis comparing 39 schizophrenia patients with severe TD to 3118 unrelated population controls. Then, we checked the potential top candidate genes among 43 patients without any TD manifestations. All the genes that were found to harbor one or more qualifying variants in patients without any TD features were excluded from the final list of candidate genes. Only one gene, regulating synaptic membrane exocytosis 2 (RIMS2), showed significant enrichment of qualifying variants in TD patients compared with unrelated population controls after correcting for multiple testing (Fisher's exact test p = 5.32E-08, logistic regression p = 2.50E-08). Enrichment was caused by a single variant (rs567070433) due to a frameshift in an alternative transcript of RIMS2. None of the TD negative patients had qualifying variants in this gene. In a validation cohort of 140 schizophrenia patients assessed for TD, the variant was also not detected in any individual. Some potentially suggestive TD genes were detected in the TD cohort and warrant follow-up in future studies. No significant enrichment in previously reported TD candidate genes was identified. To the best of our knowledge, this is the first WES study of TD, demonstrating the potential role of rare loss-of-function variant enrichment in this pharmacogenetic phenotype.

Integrating Next-Generation Sequencing in the Clinical Pharmacogenomics Workflow

Pharmacogenomics has been recognized as a fundamental tool in the era of personalized medicine with up to 266 drug labels, approved by major regulatory bodies, currently containing pharmacogenomics information. Next-generation sequencing analysis assumes a critical role in personalized medicine, providing a comprehensive profile of an individual's variome, particularly that of clinical relevance, comprising of pathogenic variants and pharmacogenomic biomarkers. Here, we propose a strategy to integrate next-generation sequencing into the current clinical pharmacogenomics workflow from deep resequencing to pharmacogenomics consultation, according to the existing guidelines and recommendations.

Ensuring privacy and security of genomic data and functionalities

In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities—computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

Association of FAM65B, AGBL4, and CUX2 genetic polymorphisms with susceptibility to antituberculosis drug-induced hepatotoxicity: validation study in a Chinese Han population

OBJECTIVE

Antituberculosis (anti-TB) drug-induced hepatotoxicity (ATDH) is a serious adverse drug reaction, and its pathogenic mechanism has not been elucidated thoroughly to date. A recent genome-wide association study reported that seven single-nucleotide polymorphisms (SNPs) in the family with sequence similarity 65, member B gene (FAM65B), ATP/GTP-binding protein-like 4 gene (AGBL4), and cut-like homeobox 2 gene (CUX2) were associated strongly with ATDH in Ethiopian patients. We validated this relationship in a Chinese Han anti-TB treatment population.

PATIENTS AND METHODS

A 1 : 2 matched case-control study was carried out of 235 ATDH cases and 470 controls. Multivariate conditional logistic regression analysis was used to estimate the association between genotypes and risk of ATDH by odds ratios with 95% confidence intervals, and weight and hepatoprotectant use were used as covariates.

RESULTS

Patients with a polymorphism at rs10946737 in the FAM65B gene were at an increased risk of moderate and severe liver injury under the dominant model (adjusted odds ratio=2.147, 95% confidence interval: 1.067-4.323, P=0.032). No other genotypes or genetic risk scores were found to be significantly related to ATDH.

CONCLUSION

This is the first study to explore and validate the relationships between seven SNPs in the FAM65B, AGBL4, and CUX2 genes and ATDH in a Chinese population. On the basis of this case-control study, SNP rs10946737 in FAM65B may be associated with susceptibility to ATDH in Chinese Han anti-TB treatment patients. Further research is warranted to explain the role of the FAM65B gene and its contribution toward individual differences in susceptibility to ATDH.

The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics

Inherited genetic variations in pharmacogenetic loci are widely acknowledged as important determinants of phenotypic differences in drug response, and may be actionable in the clinic. However, recent studies suggest that a considerable number of novel rare variants in pharmacogenes likely contribute to a still unexplained fraction of the observed interindividual variability. Next-generation sequencing (NGS) represents a rapid, relatively inexpensive, large-scale DNA sequencing technology with potential relevance as a comprehensive pharmacogenetic genotyping platform to identify genetic variation related to drug therapy. However, many obstacles remain before the clinical use of NGS-based test results, including technical challenges, functional interpretation, and strict requirements for diagnostic tests. Advanced computational analyses, high-throughput screening methodologies, and generation of shared resources with cell-based and clinical information will facilitate the integration of NGS data into candidate genotyping approaches, likely enhancing future drug phenotype predictions in patients.

Comparative study of the effect of atorvastatin and fenofibrate on high-density lipoprotein cholesterol levels in patients with type 2 diabetes

Diabetes is the most common metabolic disease. Type 2 diabetes is a variable combination of insulin resistance and disorder in insulin secretion, leading to disorder of lipids and plasma lipoproteins. The most common pattern of dyslipidemia in diabetic is high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C). This study was conducted to find a more effective drug to increase HDL-C. In this study, 80 patients (26 males and 54 females) with type 2 diabetes received fenofibrate in cross-sectional way for 2 months, and they did not take antilipid drugs for 2 month. Then, they underwent atorvastatin for 2 months and HDL-C was measured before and after taking drugs. Patients did not change their diet during this study. Effect of atorvastatin and fenofibrate on HDL-C levels in patients with type 2 diabetes was evaluated. The mean HDL-C and total cholesterol (TC) before and after taking drugs were 36.5 mg/dL and 174.56 mg/dL, respectively. After atorvastatin, the mean HDL-C and TC were 43.30 and 150.144 mg/dL, respectively, and after fenofibrate, 43.40 were mg/dL and 146.36 mg/dL, respectively. Atorvastatin caused increase in HDL-C by 18.44% and reduction in TC by 13.82% and fenofibrate increase in HDL-C by18.62% and reduction in TC by 16.05%. No difference was seen between atorvastatin and fenofibrate in terms of effect on the HDL-C excess (P = 0.449). In addition, no difference was seen between atorvastatin and fenofibrate in terms of effect on TC reduction (P = 0.992). In conclusion various factors are involved in increasing the HDL, such as race, sex, nutrition, physical activity and, of course, medications. The effect of medications is also different on races and genetics. The value of increase in HDL-C after Fenofibrate and Atorvastatin was associated with gender so that it caused more increase of HDL-C in females.

Constrained maximum entropy models to select genotype interactions associated with censored failure times

We propose a novel screening method targeting genotype interactions associated with disease risks. The proposed method extends the maximum entropy conditional probability model to address disease occurrences over time. Continuous occurrence times are grouped into intervals. The model estimates the conditional distribution over the disease occurrence intervals given individual genotypes by maximizing the corresponding entropy subject to constraints linking genotype interactions to time intervals. The EM algorithm is employed to handle observations with uncertainty, for which the disease occurrence is censored. Stepwise greedy search is proposed to screen a large number of candidate constraints. The minimum description length is employed to select the optimal set of constraints. Extensive simulations show that five or so quantile-dependent intervals are sufficient to categorize disease outcomes into different risk groups. Performance depends on sample size, number of genotypes, and minor allele frequencies. The proposed method outperforms the likelihood ratio test, Lasso, and a previous maximum entropy method with only binary (disease occurrence, non-occurrence) outcomes. Finally, a GWAS study for type 1 diabetes patients is used to illustrate our method. Novel one-genotype and two-genotype interactions associated with neuropathy are identified.

Pharmacogenetic content of commercial genome-wide genotyping arrays

Aim: We have evaluated the pharmacogenetic content of commercial human genome-wide genotyping arrays, as it is a critical determinant to enabling pharmacogenomic discoveries. Methods: Using bioinformatics approaches, we assessed 27,811 genetic variants in 3146 genes for their presence in 18 Illumina and 15 Affymetrix genome-wide arrays. Results: The pharmacogenetic content of the arrays varied greatly. The combination of the Affymetrix precision medicine array and PharmacoScan arrays (Affymetrix) had the highest coverage for a set of clinically actionable absorption, distribution, metabolism and excretion (ADME) variants, single nucleotide ADME variants and ADME insertions/deletions, with a physical coverage of 125/130 (96.2%), 9924/24,138 (41.1%) and 2252/3994 (56.4%), respectively. Conclusion: The combination of the Affymetrix precision medicine array and PharmacoScan arrays provided both genome-wide and pharmacogene coverage, which is crucial in the discovering of new variants responsible for drug adverse effects. These results will help in the design of pharmacogenomic studies and will enable a critical review of results from past studies.

Challenges in designing and executing clinical trials in a dish studies

The ever-increasing cost of drug discovery and development represents a significant challenge for the pharmaceutical industry and new strategies to bridge studies between preclinical testing and clinical trials are needed to reduce the knowledge gap prior to first human exposures, and to allow earlier decisions to be made on the further development of drugs. A number of studies have demonstrated that various cell types differentiated from human induced pluripotent stem cells (iPSCs) do not just respond similarly to human tissues in general, but rather recapitulate the drug response of their specific donor's, when exposed to the same drug in vivo. This recapitulation opens the doors to Clinical Trials in a Dish (CTiD), a platform which involves testing, in vitro, medical therapies for safety on cells collected from a sample of human patients, before moving into clinical trials. However, the science behind CTiD is complex, and every element of the process from tissue acquisition to data generation must be assessed and designed to meet quality metrics and standards. Without such rigorous assessment and design, the basic scientific integrity of CTiD constructs is likely compromised, and the results questionable. Given the lack of standard process and/or quality metrics in place for the use of stem cell-based products for in vitro testing per se, we discuss here the key elements that one needs to consider when designing, implementing and executing CTiD studies, in order to ensure an approach that will reliably mimic clinical trials, and allow obtaining reproducible and reliable experimental data.

Exploring predictive biomarkers from clinical genome-wide association studies via multidimensional hierarchical mixture models

Although the detection of predictive biomarkers is of particular importance for the development of accurate molecular diagnostics, conventional statistical analyses based on gene-by-treatment interaction tests lack sufficient statistical power for this purpose, especially in large-scale clinical genome-wide studies that require an adjustment for multiplicity of a huge number of tests. Here we demonstrate an alternative efficient multi-subgroup screening method using multidimensional hierarchical mixture models developed to overcome this issue, with application to stroke and breast cancer randomized clinical trials with genomic data. We show that estimated effect size distributions of single nucleotide polymorphisms (SNPs) associated with outcomes, which could provide clues for exploring predictive biomarkers, optimizing individualized treatments, and understanding biological mechanisms of diseases. Furthermore, using this method we detected three new SNPs that are associated with blood homocysteine levels, which are strongly associated with the risk of stroke. We also detected six new SNPs that are associated with progression-free survival in breast cancer patients.

Improved molecular karyotyping in glioblastoma

Uneven replication creates artifacts during whole genome amplification (WGA) that confound molecular karyotype assignment in single cells. Here, we present an improved WGA recipe that increased coverage and detection of copy number variants (CNVs) in single cells. We examined serial resections of glioblastoma (GBM) tumor from the same patient and found low-abundance clones containing CNVs in clinically relevant loci that were not observable using bulk DNA sequencing. We discovered extensive genomic variability in this class of tumor and provide a practical approach for investigating somatic mosaicism.

Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities

Identification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR addresses some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either before or after approval of the drug, and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized, and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.

Pharmacogenetics in Cardiovascular Medicine

Considerable interindividual variability in response to cardiovascular pharmacotherapy exists with drug responses varying from being efficacious to inadequate to induce severe adverse events. Fueled by advancements and multidisciplinary collaboration across disciplines such as genetics, bioinformatics, and basic research, the vision of personalized medicine, rather than a one-size-fits-all approach, may be within reach. Pharmacogenetics offers the potential to optimize the benefit-risk profile of drugs by tailoring diagnostic and treatment strategies according to the individual patient. To date, a multitude of studies has tried to delineate the effects of gene-drug interactions for drugs commonly used to treat cardiovascular-related disease. The focus of this review is on how genetic variability may modify drug responsiveness and patient outcomes following therapy with commonly used cardiovascular drugs including clopidogrel, warfarin, statins, and β-blockers. Also included are examples of how genetic studies can be used to guide drug discovery and examples of how genetic information may be deployed in clinical decision making.

Associations between genetic polymorphisms of membrane transporter genes and prognosis after chemotherapy: meta-analysis and finding from Seoul Breast Cancer Study (SEBCS)

Membrane transporters can be major determinants of the pharmacokinetic profiles of anticancer drugs. The associations between genetic variations of ATP-binding cassette (ABC) and solute carrier (SLC) genes and cancer survival were investigated through a meta-analysis and an association study in the Seoul Breast Cancer Study (SEBCS). Including the SEBCS, the meta-analysis was conducted among 38 studies of genetic variations of transporters on various cancer survivors. The population of SEBCS consisted of 1338 breast cancer patients who had been treated with adjuvant chemotherapy. A total of 7750 SNPs were selected from 453 ABC and/or SLC genes typed by an Affymetrix 6.0 chip. ABCB1 rs1045642 was associated with poor progression-free survival in a meta-analysis (HR = 1.33, 95% CI: 1.07-1.64). ABCB1, SLC8A1, and SLC12A8 were associated with breast cancer survival in SEBCS (P_gene < 0.05). ABCB1 rs1202172 was differentially associated with survival depending on the chemotherapy (P_interaction = 0.035). Our finding provides suggestive associations of membrane transporters on cancer survival.

Genetic variation in human drug-related genes

Background

Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied.

Methods

Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations.

Results

We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents.

Conclusions

This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0502-5) contains supplementary material, which is available to authorized users.

Rethinking ovarian cancer genomics: where genome-wide association studies stand?

Genome-wide association studies (GWAS) allow the finding of genetic variants associated with several traits. Regarding ovarian cancer (OC), 15 GWAS have been conducted since 2009, with the discovery of 49 SNPs associated with disease susceptibility and 46 with impact in the clinical outcome of patients (p < 5.00 × 10-2). Among them, 14 variants reached the genome-wide significance (p < 5.00 × 10−8). Despite the results obtained, they should be validated in independent sets. So far, five validation studies have been conducted which could confirm the association of 12 OC susceptibility SNPs. Consequently, post-GWAS studies are crucial unravel the biological plausibility of GWAS’ findings and the allelic spectrum of OC.

Pharmacogenetics in Cardiovascular Medicine

PURPOSE OF REVIEW

Pharmacogenetics is an important component of precision medicine. Even within the genomic era, several challenges lie ahead in the road towards clinical implementation of pharmacogenetics in the clinic. This review will summarize the current state of knowledge regarding pharmacogenetics of cardiovascular drugs, focusing on those with the most evidence supporting clinical implementation- clopidogrel, warfarin and simvastatin.

RECENT FINDINGS

There is limited translation of pharmacogenetics into clinical practice primarily due to the absence of outcomes data from prospective, randomized, genotype-directed clinical trials. There are several ongoing randomized controlled trials that will provide some answers as to the clinical utility of genotype-directed strategies. Several academic medical centers have pushed towards clinical implementation where the clinical validity data are strong. Their experiences will inform operational requirements of a clinical pharmacogenetics testing including the timing of testing, incorporation of test results into the electronic health record, reimbursement and ethical issues.

SUMMARY

Pharmacogenetics of clopidogrel, warfarin and simvastatin are three examples where pharmacogenetics testing may provide added clinical value. Continued accumulation of evidence surrounding clinical utility of pharmacogenetics markers is imperative as this will inform reimbursement policy and drive adoption of pharamcogenetics into routine care.

Clinical Trial Design in Juvenile Idiopathic Arthritis

Randomized clinical trials provide the gold standard evidence base to guide clinical practice. Despite major advances in trial design, pediatric clinical trials are still difficult to perform and pose unique challenges, including the need to consider the impact of developmental changes in trial design. Advances within pediatric rheumatology combined with the need to comply with legislative requirements have driven new approaches to performing pediatric clinical trials such as utilization of large research networks, incorporation of patient and family stakeholders in the planning and implementation of clinical trials, and the development of novel trial designs. The expansion of available biological therapeutics that now includes biosimilar drugs highlights the important and difficult balance of providing new and cost-effective drugs to children while ensuring safety in a vulnerable population. Future advances in juvenile idiopathic arthritis (JIA) clinical trials will likely be the application of precision medicine based on biologic, rather than phenotypic, classification of JIA, with improved understanding of pediatric clinical pharmacology. Clinical trial simulations and comparative effectiveness studies are important supplements to traditional clinical trials, permitting efficient studies and results that are more generalizable.

Next generation sequencing: implications in personalized medicine and pharmacogenomics

A breakthrough in next generation sequencing (NGS) in the last decade provided an unprecedented opportunity to investigate genetic variations in humans and their roles in health and disease. NGS offers regional genomic sequencing such as whole exome sequencing of coding regions of all genes, as well as whole genome sequencing. RNA-seq offers sequencing of the entire transcriptome and ChIP-seq allows for sequencing the epigenetic architecture of the genome. Identifying genetic variations in individuals can be used to predict disease risk, with the potential to halt or retard disease progression. NGS can also be used to predict the response to or adverse effects of drugs or to calculate appropriate drug dosage. Such a personalized medicine also provides the possibility to treat diseases based on the genetic makeup of the patient. Here, we review the basics of NGS technologies and their application in human diseases to foster human healthcare and personalized medicine.

Personalized medicine: Genetic risk prediction of drug response

Pharmacogenomics (PGx), a substantial component of "personalized medicine", seeks to understand each individual's genetic composition to optimize drug therapy -- maximizing beneficial drug response, while minimizing adverse drug reactions (ADRs). Drug responses are highly variable because innumerable factors contribute to ultimate phenotypic outcomes. Recent genome-wide PGx studies have provided some insight into genetic basis of variability in drug response. These can be grouped into three categories. [a] Monogenic (Mendelian) traits include early examples mostly of inherited disorders, and some severe (idiosyncratic) ADRs typically influenced by single rare coding variants. [b] Predominantly oligogenic traits represent variation largely influenced by a small number of major pharmacokinetic or pharmacodynamic genes. [c] Complex PGx traits resemble most multifactorial quantitative traits -- influenced by numerous small-effect variants, together with epigenetic effects and environmental factors. Prediction of monogenic drug responses is relatively simple, involving detection of underlying mutations; due to rarity of these events and incomplete penetrance, however, prospective tests based on genotype will have high false-positive rates, plus pharmacoeconomics will require justification. Prediction of predominantly oligogenic traits is slowly improving. Although a substantial fraction of variation can be explained by limited numbers of large-effect genetic variants, uncertainty in successful predictions and overall cost-benefit ratios will make such tests elusive for everyday clinical use. Prediction of complex PGx traits is almost impossible in the foreseeable future. Genome-wide association studies of large cohorts will continue to discover relevant genetic variants; however, these small-effect variants, combined, explain only a small fraction of phenotypic variance -- thus having limited predictive power and clinical utility.

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.

Genetics of primary sclerosing cholangitis and pathophysiological implications

Primary sclerosing cholangitis (PSC) is a chronic disease leading to fibrotic scarring of the intrahepatic and extrahepatic bile ducts, causing considerable morbidity and mortality via the development of cholestatic liver cirrhosis, concurrent IBD and a high risk of bile duct cancer. Expectations have been high that genetic studies would determine key factors in PSC pathogenesis to support the development of effective medical therapies. Through the application of genome-wide association studies, a large number of disease susceptibility genes have been identified. The overall genetic architecture of PSC shares features with both autoimmune diseases and IBD. Strong human leukocyte antigen gene associations, along with several susceptibility genes that are critically involved in T-cell function, support the involvement of adaptive immune responses in disease pathogenesis, and position PSC as an autoimmune disease. In this Review, we survey the developments that have led to these gene discoveries. We also elaborate relevant interpretations of individual gene findings in the context of established disease models in PSC, and propose relevant translational research efforts to pursue novel insights.