Insights from genome-wide association studies of drug response

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans

INTRODUCTION

Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response.

METHOD

Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes.

RESULTS

The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess.

LIMITATIONS

Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted.

CONCLUSION

Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00265291.

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.

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.

The Role of Pharmacogenomics in Bipolar Disorder: Moving Towards Precision Medicine

Bipolar disorder (BD) is a common and disabling psychiatric condition with a severe socioeconomic impact. BD is treated with mood stabilizers, among which lithium represents the first-line treatment. Lithium alone or in combination is effective in 60% of chronically treated patients, but response remains heterogenous and a large number of patients require a change in therapy after several weeks or months. Many studies have so far tried to identify molecular and genetic markers that could help us to predict response to mood stabilizers or the risk for adverse drug reactions. Pharmacogenetic studies in BD have been for the most part focused on lithium, but the complexity and variability of the response phenotype, together with the unclear mechanism of action of lithium, limited the power of these studies to identify robust biomarkers. Recent pharmacogenomic studies on lithium response have provided promising findings, suggesting that the integration of genome-wide investigations with deep phenotyping, in silico analyses and machine learning could lead us closer to personalized treatments for BD. Nevertheless, to date none of the genes suggested by pharmacogenetic studies on mood stabilizers have been included in any of the genetic tests approved by the Food and Drug Administration (FDA) for drug efficacy. On the other hand, genetic information has been included in drug labels to test for the safety of carbamazepine and valproate. In this review, we will outline available studies investigating the pharmacogenetics and pharmacogenomics of lithium and other mood stabilizers, with a specific focus on the limitations of these studies and potential strategies to overcome them. We will also discuss FDA-approved pharmacogenetic tests for treatments commonly used in the management of BD.

Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing

Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.

High-Definition Medicine

The foundation for a new era of data-driven medicine has been set by recent technological advances that enable the assessment and management of human health at an unprecedented level of resolution-what we refer to as high-definition medicine. Our ability to assess human health in high definition is enabled, in part, by advances in DNA sequencing, physiological and environmental monitoring, advanced imaging, and behavioral tracking. Our ability to understand and act upon these observations at equally high precision is driven by advances in genome editing, cellular reprogramming, tissue engineering, and information technologies, especially artificial intelligence. In this review, we will examine the core disciplines that enable high-definition medicine and project how these technologies will alter the future of 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.

Impact of New Genomic Technologies on Understanding Adverse Drug Reactions

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

Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery

Genomic studies have greatly advanced our understanding of the multifactorial aetiology of type 2 diabetes mellitus (T2DM) as well as the multiple subtypes of monogenic diabetes mellitus. In this Review, we discuss the existing pharmacogenetic evidence in both monogenic diabetes mellitus and T2DM. We highlight mechanistic insights from the study of adverse effects and the efficacy of antidiabetic drugs. The identification of extreme sulfonylurea sensitivity in patients with diabetes mellitus owing to heterozygous mutations in HNF1A represents a clear example of how pharmacogenetics can direct patient care. However, pharmacogenomic studies of response to antidiabetic drugs in T2DM has yet to be translated into clinical practice, although some moderate genetic effects have now been described that merit follow-up in trials in which patients are selected according to genotype. We also discuss how future pharmacogenomic findings could provide insights into treatment response in diabetes mellitus that, in addition to other areas of human genetics, facilitates drug discovery and drug development for T2DM.

Uncovering drug-responsive regulatory elements

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

A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis

AIM

A regulatory network in the human brain mediating lithium response in bipolar patients was revealed by analysis of functional SNPs from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping.

METHODS

An initial set of 23,312 SNPs in linkage disequilibrium with lead SNPs, and sub-threshold GWAS SNPs rescued by pathway analysis, were studied in the same populations. These were assessed using our workflow and annotation by the epigenome roadmap consortium.

RESULTS

Twenty-seven percent of 802 SNPs that were associated with lithium response (13 published studies gene association studies and two GWAS) were shared in common with 1281 SNPs from 18 GWAS examining psychiatric disorders and adverse events associated with lithium treatment. Nineteen SNPs were annotated as active regulatory elements such as enhancers and promoters in a tissue-specific manner. They were located within noncoding regions of ten genes: ANK3, ARNTL, CACNA1C, CACNG2, CDKN1A, CREB1, GRIA2, GSK3B, NR1D1 and SLC1A2. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p = 10(-27); Fisher exact test) with an AMPA2 glutamate receptor network in human brain. Our workflow results showed concordance with annotation of regulatory elements from the epigenome roadmap. Analysis of cognate mRNA and enhancer RNA exhibited patterns consistent with an integrated pathway in human brain.

CONCLUSION

This pharmacoepigenomic regulatory pathway is located in the same brain regions that exhibit tissue volume loss in bipolar disorder. Although in silico analysis requires biological validation, the approach provides value for identification of candidate variants that may be used in pharmacogenomic testing to identify bipolar patients likely to respond to lithium.

Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response

AIM

To provide insight into potential regulatory mechanisms of gene expression underlying addiction, analgesia, psychotropic drug response and adverse drug events, genome-wide association studies searching for variants associated with these phenotypes has been undertaken with limited success. We undertook analysis of these results with the aim of applying epigenetic knowledge to aid variant discovery and interpretation.

METHODS

We applied conditional imputation to results from 26 genome-wide association studies and three candidate gene-association studies. The analysis workflow included data from chromatin conformation capture, chromatin state annotation, DNase I hypersensitivity, hypomethylation, anatomical localization and biochronicity. We also made use of chromatin state data from the epigenome roadmap, transcription factor-binding data, spatial maps from published Hi-C datasets and 'guilt by association' methods.

RESULTS

We identified 31 pharmacoepigenomic SNPs from a total of 2024 variants in linkage disequilibrium with lead SNPs, of which only 6% were coding variants. Interrogation of chromatin state using our workflow and the epigenome roadmap showed agreement on 34 of 35 tissue assignments to regulatory elements including enhancers and promoters. Loop boundary domains were inferred by association with CTCF (CCCTC-binding factor) and cohesin, suggesting proximity to topologically associating domain boundaries and enhancer clusters. Spatial interactions between enhancer-promoter pairs detected both known and previously unknown mechanisms. Addiction and analgesia SNPs were common in relevant populations and exhibited large effect sizes, whereas a SNP located in the promoter of the SLC1A2 gene exhibited a moderate effect size for lithium response in bipolar disorder in patients of European ancestry. SNPs associated with drug-induced organ injury were rare but exhibited the largest effect sizes, consistent with the published literature.

CONCLUSION

This work demonstrates that an in silico bioinformatics-based approach using integrative analysis of a diversity of molecular and morphological data types can discover pharmacoepigenomic variants that are suitable candidates for further validation in cell lines, animal models and human clinical trials.

Biomarkers for Bisphosphonate-Related Osteonecrosis of the Jaw

PURPOSE

The aim of this study was to investigate a possible biomarker for bisphosphonate-related osteonecrosis of the jaw (BRONJ) in an animal model.

MATERIALS AND METHODS

Forty-eight Sprague-Dawley rats were randomly divided into the bisphosphonate group (n = 36), who were injected once a week with zoledronic acid, and the control group (n = 12), who were injected once a week with saline. After 6 weeks, surgical intervention was performed, and injections were continued up to 8 weeks. Rats in the bisphosphonate group were then further classified to the ONJ group, and the non-ONJ group, and biomarkers, including CTx, Glu-OC, TRACP 5b, RANKL, and OPG, were assessed at baseline (T0), at surgical intervention (T1), and at sacrifice (T2). Histomorphometric analysis for quantification of osteoclasts was performed.

RESULTS

Repeated measures analysis of variance revealed that TRACP 5b levels and the RANKL/OPG ratio were significantly decreased over time in the ONJ group compared with the non-ONJ group (p < .05). At T2, the area under the curve was 0.807 for TRACP 5b (sensitivity: 88.9%, specificity 66.7% at cutoff) and 0.765 for the RANKL/OPG ratio (sensitivity: 77.8%, specificity 62.9% at cutoff). TRACP 5b showed a lower least significant change (29.6%) with lower intra-assay coefficient of variability (CV; 6.32%) and interassay CV (11.20%) compared with those of the RANKL/OPG ratio (39.27%) and showed a higher signal-to-noise ratio (2.76) than that of the RANKL/OPG ratio (1.62). N.Oc/T.Ar and N.Oc/B.Ar demonstrated significantly decreased number of osteoclasts in ONJ group versus non-ONJ group.

CONCLUSIONS

These results show that serum TRACP 5b and the RANKL/OPG ratio were possible biomarkers for BRONJ. These data may provide useful additional information for future ONJ research. Further studies are needed to validate these results in humans with ONJ.

Beta cell connectivity in pancreatic islets: a type 2 diabetes target?

Beta cell connectivity describes the phenomenon whereby the islet context improves insulin secretion by providing a three-dimensional platform for intercellular signaling processes. Thus, the precise flow of information through homotypically interconnected beta cells leads to the large-scale organization of hormone release activities, influencing cell responses to glucose and other secretagogues. Although a phenomenon whose importance has arguably been underappreciated in islet biology until recently, a growing number of studies suggest that such cell-cell communication is a fundamental property of this micro-organ. Hence, connectivity may plausibly be targeted by both environmental and genetic factors in type 2 diabetes mellitus (T2DM) to perturb normal beta cell function and insulin release. Here, we review the mechanisms that contribute to beta cell connectivity, discuss how these may fail during T2DM, and examine approaches to restore insulin secretion by boosting cell communication.

Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations

Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.

Toxicogenetics: in search of host susceptibility to environmental toxicants

Heavy metals, various pesticide and herbicides are implicated as risk factors for human health. Paraquat, maneb, and rotenone, carbamate, and organophosphorous insecticides are examples of toxicants for which acute and chronic exposure are associated with multiple neurological disorders including Parkinson’s disease. Nevertheless, the role of pesticide exposure in neurodegenerative diseases is not clear-cut, as there are inconsistencies in both the epidemiological and preclinical research. The aim of this short review is to show that at least, some of the inconsistencies are related to individual differences in susceptibility to the effects of neurotoxicants, individual differences that can be traced to the genetic constitution of the individuals and animals studies, i.e., host-based susceptibility.

Considerations for rare variants in drug metabolism genes and the clinical implications

INTRODUCTION

Large-scale whole genome and exome resequencing studies have revealed that humans have a high level of deleterious rare variation, which has important implications for the design of future pharmacogenetics studies.

AREAS COVERED

Current pharmacogenetic guidelines focus on the implementation of common variation into dosing guidelines. However, it is becoming apparent that rare variation may also play an important role in differential drug response. Current sequencing technologies offer the opportunity to examine rare variation, but there are many challenges associated with such analyses. Nonetheless, if a comprehensive picture of the role that genetic variants play in treatment outcomes is to be obtained, it will be necessary to include the entire spectrum of variation, including rare variants, into pharmacogenetic research.

EXPERT OPINION

In order to implement pharmacogenetics in the clinic, patients should be genotyped for clinically actionable pharmacogenetic variants and patients responding unfavourably to treatment after pharmacogenetics-based dosing should be identified and resequenced to identify additional functionally relevant variants, including rare variants. All derived information should be added to a central database to allow for the updating of existing dosing guidelines. By routinely implementing such strategies, pharmacogenetics-based treatment guidelines will continue to improve.

Pharmacogenetic considerations for acute lymphoblastic leukemia therapies

INTRODUCTION

Advances in our understanding of the pathobiology of childhood acute lymphoblastic leukemia (ALL) have led to risk-targeted treatment regimens and remarkable improvement in survival rates. Still, up to 20% of patients experience treatment failure due to drug resistance. Treatment-related toxicities are often life-threatening and are the primary cause of treatment interruption, while ALL survivors may develop complications due to exposure to chemotherapy and/or irradiation during a vulnerable period of development. Different factors may contribute to variable treatment outcomes including patient genetics that has been shown to play important role.

AREAS COVERED

This review summarizes candidate gene and genome-wide association studies that identified common polymorphisms underlying variability in treatment responses including a few studies addressing late effects of the treatment. Genetic variants influencing antileukemic drug effects or leukemic cell biology have been identified, including for example variants in folate-dependent enzymes, influx and efflux transporters, metabolizing enzymes, drug receptor or apoptotic proteins.

EXPERT OPINION

Many pharmacogenetic studies have been conducted in ALL and a variety of potential markers have been identified. Yet more comprehensive insight into genome variations influencing drug responses is needed. Whole exome/genome sequencing, careful study design, mechanistic explanation of association found and collaborative studies will ultimately lead to personalized treatment and improved therapeutic and health outcomes.

Systems pharmacology identifies drug targets for Stargardt disease-associated retinal degeneration

A systems pharmacological approach that capitalizes on the characterization of intracellular signaling networks can transform our understanding of human diseases and lead to therapy development. Here, we applied this strategy to identify pharmacological targets for the treatment of Stargardt disease, a severe juvenile form of macular degeneration. Diverse GPCRs have previously been implicated in neuronal cell survival, and crosstalk between GPCR signaling pathways represents an unexplored avenue for pharmacological intervention. We focused on this receptor family for potential therapeutic interventions in macular disease. Complete transcriptomes of mouse and human samples were analyzed to assess the expression of GPCRs in the retina. Focusing on adrenergic (AR) and serotonin (5-HT) receptors, we found that adrenoceptor α 2C (Adra2c) and serotonin receptor 2a (Htr2a) were the most highly expressed. Using a mouse model of Stargardt disease, we found that pharmacological interventions that targeted both GPCR signaling pathways and adenylate cyclases (ACs) improved photoreceptor cell survival, preserved photoreceptor function, and attenuated the accumulation of pathological fluorescent deposits in the retina. These findings demonstrate a strategy for the identification of new drug candidates and FDA-approved drugs for the treatment of monogenic and complex diseases.

Rapid collection of biospecimens by automated identification of patients eligible for pharmacoepigenetic studies

Epigenetics plays an important role in regulating gene expression, and can be modified by environmental factors and physiological conditions. Studying epigenetics is a promising approach to potentially improving the diagnosis, prevention and treatment of human diseases, and to providing personalized medical care. However, the role of epigenetics in the development of diseases is not clear because epigenetic markers may be both mediators and outcomes of human diseases. It is particularly complicated to study pharmacoepigenetics, as medication use may modify the epigenetic profile. To address the challenges facing pharmacoepigenetic research of human diseases, we developed a novel design to rapidly identify, contact, and recruit participants and collect specimens for longitudinal studies of pharmacoepigenetics. Using data in real-time from electronic medical record systems, we can identify patients recently start on new medications and who also have a blood test. Prior to disposal of the leftover blood by the clinical laboratory, we are able to contact and recruit these patients, enabling us to use both their leftover baseline blood sample as well as leftover specimens at future tests. With treatment-naïve and follow-up specimens, this system is able to study both epigenetic markers associated with disease without treatment effect as well as treatment-related epigenetic changes.

A genome-wide association study and biological pathway analysis of epilepsy prognosis in a prospective cohort of newly treated epilepsy

We present the analysis of a prospective multicentre study to investigate genetic effects on the prognosis of newly treated epilepsy. Patients with a new clinical diagnosis of epilepsy requiring medication were recruited and followed up prospectively. The clinical outcome was defined as freedom from seizures for a minimum of 12 months in accordance with the consensus statement from the International League Against Epilepsy (ILAE). Genetic effects on remission of seizures after starting treatment were analysed with and without adjustment for significant clinical prognostic factors, and the results from each cohort were combined using a fixed-effects meta-analysis. After quality control (QC), we analysed 889 newly treated epilepsy patients using 472 450 genotyped and 6.9 × 10(6) imputed single-nucleotide polymorphisms. Suggestive evidence for association (defined as Pmeta < 5.0 × 10(-7)) with remission of seizures after starting treatment was observed at three loci: 6p12.2 (rs492146, Pmeta = 2.1 × 10(-7), OR[G] = 0.57), 9p23 (rs72700966, Pmeta = 3.1 × 10(-7), OR[C] = 2.70) and 15q13.2 (rs143536437, Pmeta = 3.2 × 10(-7), OR[C] = 1.92). Genes of biological interest at these loci include PTPRD and ARHGAP11B (encoding functions implicated in neuronal development) and GSTA4 (a phase II biotransformation enzyme). Pathway analysis using two independent methods implicated a number of pathways in the prognosis of epilepsy, including KEGG categories 'calcium signaling pathway' and 'phosphatidylinositol signaling pathway'. Through a series of power curves, we conclude that it is unlikely any single common variant explains >4.4% of the variation in the outcome of newly treated epilepsy.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

Next generation sequencing and rare genetic variants: from human population studies to medical genetics

The allelic frequency spectrum emerging from several Next Generation Sequencing (NGS) projects is revealing important details about evolutionary and demographic forces that shaped the human genome. Herein, we discuss some of the achievements of the use of low-frequency and rare variants from NGS studies. The majority of variants that affect protein-coding regions are recent and rare. Often, the novel rare variants are enriched for deleterious alleles and are population-specific, making them suitable for the study of disease susceptibility. To investigate this kind of variation and its effects in association studies, very large sample sizes will be necessary to achieve sufficient statistical power. Moreover, as these variants are typically population-specific, the replication of disease associations across populations could be very difficult due to population stratification. Therefore, the design of experiments focusing on the identification of rare variants and their effects should be carefully planned. Although several successes have already been achieved through NGS for genetic epidemiology, pharmacogenetic and clinical purposes, with improvements of the sequencing technology and decreased costs, further advances are expected in the near future.