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Genome‐wide and Phenome‐wide Approaches to Understand Variable Drug Actions in Electronic Health Records

Year: 2017

Type: review

Abstract: Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) have provided powerful methods for investigating the impact of genetic variation on individual drug response and have added extensive knowledge to the understanding of drug targets and effects. We highlight here recent advances in drug development, repurposing, and personalization accelerated by applying GWAS and PheWAS to longitudinal health data information, along with limitations of these methods. The challenges facing modern clinical pharmacology can be grouped into two major categories: the efficient development of novel therapeutics and understanding individual variability in response. The development of novel therapeutics is hampered by the problem that despite major advances in the knowledge of disease mechanisms, drug targets, and biomarkers, as well as a continual rise in investment into pharmaceutical research, the number of new drugs approved each year has remained steady.1 Most drugs fail in phase II clinical trials, with 50% of the failures due to lack of efficacy.2, 3 Thus, there is concern that preclinical disease models do not reliably predict efficacy in patients. Human genetics has been proposed as a mechanism to prioritize molecular targets early in the stages of drug discovery towards potentially more efficacious models.4, 5 The problem of variable drug efficacy and susceptibility to side effects has been recognized since the advent of therapeutics. There is now evidence that medication exposure data, outcome data, and genetic information linked together via longitudinal electronic health records (EHRs) can provide a more thorough understanding of drug effects, including response patterns and individuals at risk for potentially rare side effects.6 Knowledge of the genetic mechanisms that drive drug response variation and adverse events can help guide the tailoring of medication therapy. GWAS and PheWAS are modern genetic tools for the exploration of data sets in order to efficiently identify potential targets for novel therapeutics and to provide evidence-based individualized therapy. GWAS is a hypothesis-generating method to systematically analyze variants across the entire genome (i.e., "genome-wide") for association with a phenotype of interest (Figure 1a). Over the past 10 years, the genotyping assays have evolved from early versions assessing hundreds of thousands of single nucleotide polymorphisms (SNPs) to current panels including millions of SNPs.7 At the same time, pipelines for quality control, imputation of genotypes, and statistical analysis for dichotomous, categorical, and continuous traits have matured and become standardized across high-quality laboratories. The threshold for statistically significant results, given the need to correct for multiple comparisons, has been established at 5 × 10−8. Despite this stringent threshold, use of larger and larger cohorts (including some recent cohorts including more than 700,000 individuals) has enabled identification of many SNPs with statistically significant P-values.8 GWAS focuses on detection of associations with relatively common variants (e.g., minor allele frequencies 1–5%) so the odds ratios are often small (OR < 1.5). Thus, the major outcome of many GWAS is a better understanding of the genetic architecture of complex traits, but not a set of high effect size variants that would be clinically actionable.7 As described below, examining drug response with GWAS has provided an interesting counterexample to these generalizations, since small numbers can yield signals with large enough effect sizes to be considered for implementation. This may reflect the idea that drug response represents an example of a controlled environmental intervention (drug) interacting with a genome, rather than a multifactorial complex disease state with many potential environmental inputs. Since 2005, over 3,000 GWAS have identified almost 40,000 unique SNP-trait associations within the GWAS catalog provided by the National Human Genome Research Institute (NHGRI) and European Bioinformatics Institute (EBI).7 The rapid increase in knowledge of common genetic variants in complex diseases has provided significant opportunities for analyzing the association of genetic variation with disease phenotypes and response to therapies. The integration of this wealth of genetic information with phenotypic data by linkage of DNA biobanks with EHRs has led to the development of a reverse genetics approach with EHR-based genomic studies, termed PheWAS (Figure 1b).6 The first PheWAS study was performed in 2010, in which Denny et al. successfully replicated four known SNP-disease associations.9 Since then, the use of PheWAS has continued to rise in popularity, with 58 current PubMed indexed publications. PheWAS provides a systematic approach to analyze the many phenotypes potentially associated with a specific genotype, with the ability to identify pleiotropy, or the finding of multiple independent associations with a single genetic association.10 The threshold for statistical significance is less well established for PheWAS; therefore, often a Bonferroni correction is applied in the analyses. However, this is highly stringent, as it assumes independence across all phenotypes, which is unlikely, given that many phenotypes are closely related. Despite this, the use of large cohorts have allowed for PheWAS to not only replicate known findings, but also identify novel associations. GWAS and PheWAS approaches are complementary, with the ability to replicate and validate the other's findings. Representing the capacity for PheWAS to replicate GWAS, a comprehensive comparison of known GWAS associations within the NHGRI GWAS catalog against the PheWAS method was performed in 2013, showing that 210 of 751 (28%) known SNP-disease associations were replicated with PheWAS, including 66% of those associations that were adequately powered to detect the association.11 This method also identified 63 potentially novel SNP-disease associations, again demonstrating pleiotropic effects of the variants. In a pediatric cohort, PheWAS replicated many prior known GWAS associations, including SNPs associated with juvenile rheumatoid arthritis, asthma, autism, and pervasive developmental disorder, and type 1 diabetes.12 Several new SNP-disease associations were identified within the pediatric population as well, including a cluster of association near the NDFIP1 gene (associated with mental retardation), PLCL1 (developmental delay), and the IL5-IL13 region (eosinophilic esophagitis).12 The EHR provides a longitudinal collection of phenotypic information coupled with medication exposures, thus making it an important platform for study of drug effects.6 A broad set of phenotypes collected in an unbiased approach is essential to the PheWAS method.13 To accomplish this, many PheWAS have used phenotypes derived from custom groupings of billing codes, also referred to as phecodes.9 The billing codes used for phecode groupings currently are International Classification of Diseases, Ninth Revision (ICD-9) codes. ICD codes are published and maintained by the World Health Organization for classification of diseases and services for reimbursement of medical services. While the phecode groupings have been shown to better align with clinical diseases in practice, other methods of phenotype classification also are effective for PheWAS studies.14 Hebbring et al. reported a PheWAS method using individual ICD-9 codes and parent ICD-9 three-digit groups as phenotypes.15 They not only replicated a known association of an HLA class II allele, HLA-DRB1, with multiple sclerosis, but also replicated associations of HLA-DRB1 with erythematous conditions and benign neoplasms of the respiratory and intrathoracic organs, found to be significant in a prior study by Denny and colleagues.9, 15 These and other studies highlight the importance of PheWAS techniques for identifying pleiotropic effects. Billing codes are not the only source of phenotypes from the EHR. Hebbring et al. have shown that PheWAS can be performed by defining the phenome solely on textual data within clinical documentation.16 For drug effects, a phenome based on billing codes or clinical text alone may not accurately capture drug efficacy or adverse events, nor do they provide the necessary information about drug exposure, including dosing data. One potential method of obtaining this drug exposure and outcome data is to use prospective cohort-based studies. It has been shown that PheWAS can be used with data obtained through clinical trials, representing a more biased, but targeted, approach at defining a phenome.17, 18 The benefit of leveraging EHRs for both GWAS and PheWAS, as opposed to prospective cohort-based studies, is the ability to obtain large sample sizes with relatively less time or expense. While the EHR phenome may be incomplete, it includes conditions that are medically relevant, as opposed to clinical trial cohorts in which phenotypes may not represent conditions that necessitate medical attention. Biobanking of genetic data linked to the longitudinal patient data available within the EHR provides an efficient method for aggregating otherwise disparate information. EHR-based biobanks have the potential to integrate genomic data with medication receipt, laboratory results, or textual data, thus refining both exposures and phenotypes, essential for research on drug effects. Several features of GWAS suggest its potential for elucidating drug mechanism and identifying relevant novel drug targets. An estimated 21% of published genes within the GWAS catalog are amenable to pharmacological modulation by small molecules.19 Further, the GWAS gene set is enriched with drug targets in comparison to the entire human genome, many of which align with the disease–gene pair identified by GWAS analysis.19 Prior studies support the role of using GWAS to identify alleles that contribute to disease risk and druggable targets. Early GWAS efforts retrospectively identified the genetic basis for drugs already in use for a particular indication. Statins have been used to inhibit 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase and treat hyperlipidemia for decades,20 and GWAS studies in 2008 showed that low-density lipoprotein (LDL) levels are associated with variation in HMGCR, the gene that encodes HMG-CoA reductase.21, 22 Further, pharmacogenetics studies have shown that genetic variation in HMGCR is associated with statin efficacy.23, 24 Since then, variants in other genes involved in lipid metabolism but not direct targets for statin action (APOE, LPA, SORT1/CELSR2/PSRC1, and SLCO1B1) have been found in GWAS to be associated with the LDL-lowering effect of statin therapy.25, 26 A recent GWAS also found that several variants with the LPA locus appear to be associated with coronary heart disease events during statin therapy, independent of the extent of LDL-cholesterol lowering.27 Other examples of drugs with a mechanism replicated by GWAS are ustekinumab, a monoclonal antibody against interleukin (IL)-12 used for treatment of psoriasis and inflammatory bowel disease,28, 29 and denosumab, a monoclonal antibody to the receptor activator of nuclear factor-kappaB ligand (RANKL) for treatment of osteoporosis.30 Metformin has long been used to lower blood glucose levels in individuals with diabetes; however, a 2011 GWAS of 3,920 type 2 diabetes patients clarified the genetic basis for its mechanism with polymorphisms in the ATM gene found to be associated with glycemic control.31 Okada et al. evaluated the role of GWAS in validating the current therapeutic drug targets for rheumatoid arthritis (RA).32 Through a comprehensive genetic study with nearly 100,000 subjects, they found that 18 of 27 currently approved drugs for RA target genes identified as RA risk loci, and also suggest several potential novel therapeutics, some of which had supporting animal studies.32 These early successes fuel enthusiasm for using GWAS to elucidate disease mechanisms and drug targets.19 In the context of drug development, GWAS advances are relatively recent and are only now being applied to have a potential impact on target discovery. Nevertheless, prior linkage and candidate gene studies have shown that genetics can drive development of novel therapeutics. The development of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors represents this realization. In 2003, it was found that autosomal dominant hypercholesterolemia and an increased incidence of coronary heart disease were associated with gain-of-function mutations in the PCSK9 gene.33 Subsequent candidate gene association studies in 2005 and 2006 revealed that PCSK9 loss-of-function mutations correlate with reduced levels of LDL cholesterol and a lower incidence of coronary heart disease.34, 35 In 2012, almost 10 years after the first genetic discovery, randomized controlled trials demonstrated that PCSK9-specific monoclonal antibodies significantly reduce LDL cholesterol levels.36, 37 There are now two US Food and Drug Administration (FDA)-approved PCSK9 inhibitors. Similar to how candidate gene studies led to the target of PCSK9 and subsequent development of a novel therapy for familial hypercholesterolemia, GWAS and PheWAS hold promise as means to identify novel drug targets. However, the timeline from target to an approved drug is often over 10 years. As findings from GWAS have exponentially increased over the last decade and PheWAS is gaining similar recognition, we anticipate the next decade will show progress toward utilization of that knowledge and drug development. A considerable amount of variability can exist in a patient's response to drug therapy, including differing efficacy, adverse side effects, and toxicity. A better understanding of the genetic determinants of drug response and mechanism is thought to have potential to individualize drug treatment toward improved efficacy and side effect profiles.38 While candidate gene studies have shown success in identifying genetic variants that contribute to drug response and effects, for many drugs the biological mechanism, metabolic pathways, and potential genetic associations impacting individual response is unknown, limiting the potential for focused gene analysis. In contrast, GWAS are a hypothesis-free method that can be utilized to determine associations of genetic variation with effects of drug treatment (Figure 2a). Drug efficacy in particular is often considered to be along a continuum in a patient population. The known genetic variants contributing to statin efficacy discussed above are an indication of the significant clinical and genetic variability that can be seen in a population.25, 26 A high-yield area of pharmacogenetic investigation utilizing GWAS has been the study of drugs with a narrow therapeutic window and variable efficacious dosing regimens, such as warfarin. While candidate gene studies were used to initially describe the associations of CYP2C9 and VKORC1 with the ability for warfarin to achieve anticoagulation,39-42 subsequent GWAS have confirmed these findings, showing these to be the strongest genetic predictors of warfarin dose required in individuals of European descent.43, 44 Subsequent GWAS in individuals of African ancestry has also found that in addition to the well-known CYP2C9*2, CYP2C9*3, and VKORC1 polymorphisms, the CYP2C locus exerts influence by a variant outside of those well established, and this new variant could improve dose prediction in this population.45 Differences in the variants associated with warfarin effect across populations may be due to the differences in mean allele frequencies, ancestry-specific gene–gene interactions, or population-specific gene–environment interactions. Response to clopidogrel therapy is also known to be highly variable. Clopidogrel is a prodrug, and the bioactivation pathway is largely CYP2C19-dependent.46 Candidate gene studies of cardiovascular events on clopidogrel indicated that CYP2C19 loss-of-function variants increased risk.47-49 Subsequent GWAS in Amish individuals found that the most common loss-of-function allele, CYP2C19*2, had the strongest genetic association with the effect of clopidogrel on platelet aggregation; however, this single variant only accounts for ∼12% of the variability in response seen in this population.50 More recently, Zhong et al. identified two novel variants in a Chinese population that were associated with the antiplatelet effect of clopidogrel, as measured by P2Y12-mediated platelet aggregation, as well as formation of H4, an active metabolite of clopidogrel.51 They estimate that the identified variants, in association with CYP2C19*2, CYP2C19*3 (a variant common in Asian populations), and clinical factors, can improve the predictability of clopidogrel effect to 37.7%. Another example of GWAS elucidating the genetic underpinnings of variability in drug efficacy is in the use of interferon-alpha for treatment of hepatitis C infection. A polymorphism adjacent to IL28B has been shown to predict treatment response and viral clearance in individuals on interferon-alpha for hepatitis C in several GWAS.52-54 Because the genotype associated with improved response is more common in individuals of Asian and European ancestry than African ancestry populations, this genetic polymorphism may explain the difference in response rates between patients of African and European ancestry.52 These GWAS of statin, clopidogrel, and interferon-alpha effects all emphasize that variability in drug response is ancestry-dependent due to the vast difference in distribution of genetic variants, such as CYP2C9, CYPC19, and IL28B, across populations. These findings, along with others, have increased focus towards a personalized approach to disease treatment and encouragement of research efforts from individuals of diverse backgrounds. Studies across ancestries are needed to fully capture the genetic architecture of human traits, including drug response, and ultimately, appropriately implement such variants in clinical practice. GWAS has been used to determine potential associations of drug toxicities and adverse drug reactions (Figure 2a). The human leukocyte antigen (HLA) variation, in particular, has been associated by GWAS with susceptibility to adverse drug reactions. Drug-induced liver injury (DILI) is a rare but serious adverse effect secondary to many drug therapies, with increased susceptibility in HLA regions implicated in several studies.55-57 The first study was in 2008 and focused on ximelagatran, an oral direct thrombin inhibitor that was removed from the market in 2006 due to the development of transaminitis in some patients. In this study, Kindmark et al. performed a GWAS that suggested an association between DILI during use of ximelagatran with HLA class II alleles, which was confirmed with candidate gene studies.58 In 2009, Daly et al. found a strong association of HLA-B*5701 with DILI following treatment with flucloxacillin.55 Singer et al., in 2010, identified an association of hepatotoxicity after use of lumiracoxib, a selective cyclooxygenase-2 inhibitor, with common HLA class II haplotypes.56 In 2011, Lucena et al. performed GWAS of 201 cases of DILI after treatment with amoxicillin-clavulanate compared with 532 controls, finding HLA class I and II SNPs may confer susceptibility to liver injury after this antibiotic treatment.57 Due to the rarity of DILI, the finding of genetic predispositions in GWAS may be limited. Nicoletti et al. recently attempted to overcome this by DILI by drug other than the common and to determine for They found a strong association of with DILI, to be by the effects of This how novel methods in studies will in some cases to overcome the limitations of GWAS and rare variants with a large effect The HLA locus has also been implicated in other adverse drug including In et al. reported a strong association in a Chinese population between and by Candidate gene studies were also used to an association between variation in the HLA HLA-B*5701 (OR with which has since been both at a and Several subsequent GWAS studies across populations have shown that from to reactions such as can secondary to a of drug therapies. 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risk as PheWAS also has the potential to associations with drug effects, including therapeutic response and side effect (Figure et al. described in the use of a study population with exposure to determine associations with clinical traits after drug exposure to identify adverse They grouped individuals with exposure into based on a available from the EHR for patients with clinical They found that high was associated with diabetes and they associations with laboratory data, finding that high along with exposure was associated with an increased incidence of and by This study the ability for PheWAS to identify adverse events potentially associated with drug as well as the of of PheWAS with biological have the potential ability to the identification of pleiotropic effects through PheWAS methods to predict potential adverse events to drug therapy. et al. associations of variants, potential targets for therapeutics, for and potential adverse They first demonstrated that variants in 2 against 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gene set is enriched with targets already by drugs that align with the disease–gene pair identified by GWAS there are also in which the indication for the drug is not with the associated disease by GWAS, and examples of pleiotropy, multiple are associated with the same genetic known associations to the of drugs with known gene targets, et al. identified individual genes that are targets of drug that to a GWAS than drug These represent potential drug Prior studies have demonstrated the success of this for the use of inhibitors for the treatment of One of the first GWAS in 2005 found the gene to be associated with the risk of At that time, inhibitors had been for the treatment of and This has led to the of in the pathway in clinical trials with findings for the of In addition to validating currently approved RA drug therapies, Okada et al. identified several drugs used for other diseases that target biological genes RA risk SNPs and thus proposed these as drug They found that and targets of approved drugs for and RA risk they be for efficacy in RA as to the use of GWAS to identify novel drug the ability of PheWAS to identify pleiotropic effects opportunities for drug (Figure As PheWAS can identify diseases that a common can that drugs used to treat disease may also have efficacy to treat A hypothesis-generating study by et al. evaluated the potential for drug by current genes in to the associations in the PheWAS They the disease for drugs in but also identified that potential novel associations, available within a to the There are several that can influence the ability for a identified in PheWAS and for drug to to In particular, methods be to narrow the to candidate that are in the or by et al. this approach by all with the clinical trial that of other could also significantly improve et al. described genes with pleiotropic effects identified in PheWAS, of which are currently to study opportunities of drugs with to the relevant molecular et al. in used PheWAS methods applied to codes in the to the potential efficacy of inhibitors for the treatment of variants in the gene is associated with reduced and is common Asian populations. Through PheWAS they there was association of a loss-of-function variant in with in such as and coronary events, or diseases in a Chinese population. They that these findings correlate with the lack of efficacy in a randomized controlled trial with the inhibitor of PheWAS such as these in the of clinical trials could thus help guide study time and in GWAS and PheWAS is by the size of the

Source: Clinical and Translational Science

DOI: 10.1111/cts.12522

Authors: Jamie R. Robinson, Joshua C. Denny, Dan M. Roden, Sara L. Van Driest

Institution: Vanderbilt University Medical Center

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,630