FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2157671175> ?p ?o ?g. }

• W2157671175 endingPage "163" @default.
• W2157671175 startingPage "161" @default.
• W2157671175 abstract "PharmacogenomicsVol. 10, No. 2 Special Focus: Genome-wide association studies - EditorialFree AccessPharmacogenomic genome-wide association studies: lessons learned thus farJames J Crowley, Patrick F Sullivan and Howard L McLeodJames J Crowley† Author for correspondenceUniversity of North Carolina-Chapel Hill, 4113 Neurosciences Research Building, 103 Mason Farm Road, Chapel Hill, NC 27599, USA. Search for more papers by this authorEmail the corresponding author at crowley@unc.edu, Patrick F SullivanDepartment of Genetics and Carolina Center for Genome Sciences, University of North Carolina, NC, USASearch for more papers by this author and Howard L McLeodUniversity of North Carolina-Chapel Hill, NC, USASearch for more papers by this authorPublished Online:10 Feb 2009https://doi.org/10.2217/14622416.10.2.161AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail Over the past 4 years, the once hotly debated question of whether genome-wide genetic mapping of common SNPs would shed light on common diseases has been answered with a resounding ‘yes’. More than 100 publications have now reported the localization of common SNPs associated with a wide range of common diseases (e.g., age-related macular degeneration, Type 2 diabetes, Crohn’s disease and obesity), as well as various individual traits (height, hair color, eye color and freckling). As these publications accrued, a number of lessons regarding genetic mapping by genome-wide association studies (GWAS) began to emerge (for a review see [1]). These lessons include: effect sizes for common variants are typically modest; with currently typical sample sizes (e.g., 2000 cases and 2000 controls), the power to detect associations has been low; a single genomic region can harbor both common variants of weak effect and rare variants of large effect; most confirmed associations do not involve candidate genes suspected on the basis of prior theory; some associations implicate nonprotein-coding regions; and correlations between genetic variants and phenotypes have been limited by the accuracy and validity of the phenotypic measurement.As the number of published pharmacogenomic GWAS begins to accumulate, it is prudent to search for lessons from these early studies. A search of PubMed up to January 15th, 2009 yielded 11 articles that examined the association between a drug-induced phenotype and at least 100,000 genome-wide SNP markers. Table 1 provides an overview of these studies and their most significant findings (as always, the reader is encouraged to examine the primary literature for a more thorough description of these studies). The most prominent characteristic that distinguishes these GWAS from their disease-oriented counterparts is sample size. While disease GWAS now routinely exceed 2000 cases and 2000 controls, all of these early pharmacogenomic GWAS had sample sizes under 400 drug-treated individuals. This is not surprising considering the scarcity of available DNA samples from clinical studies, particularly for rare side effects, and the considerable expense and effort required to phenotype individuals for a drug response. While the cost per subject in a disease case–control study is generally US$500–1000, the typical cost per subject in a clinical trial ranges from $5000–30,000. While there is controversy surrounding the utility of GWAS for pharmacogenomics, we believe these early publications have demonstrated the unique power of GWAS and helped identify a set of important lessons for future studies, which will be discussed in turn.Genome-wide association studies of rare adverse drug reactions may be more likely to yield highly penetrant variantsAdverse drug reactions (ADRs) have a major impact on patients, physicians, healthcare providers, regulatory agencies and pharmaceutical companies. Identifying the genetic contributions to ADR risk may lead to a better understanding of the underlying mechanisms, identification of patients at risk, and clinical testing could lead to a decrease in ADR incidence. The Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine (SEARCH) Collaborative group carried out a GWAS in 85 subjects who developed a rare simvastatin-induced myopathy and 90 simvastatin-exposed controls who did not develop this serious side effect [2]. They identified common variants in SLCO1B1, a gene involved in statin hepatic transport, with odds ratios of 4.5 for each copy of the risk allele. Sarasquete et al. searched a large group of multiple myeloma patients for individuals who developed bisphosphonate-induced jaw osteonecrosis, a side effect that occurs in approximately 4% of patients [3]. After genotyping 22 cases and 65 matched controls, they identified SNPs in the CYP2C8 gene that may increase risk for osteonecrosis. Kindmark et al. conduced a retrospective case–control pharmacogenetic study of elevated serum alanine aminotransferase during long-term treatment with the oral direct thrombin inhibitor ximelagatran [4]. This relatively small study (74 cases and 130 treated controls) yielded a strong, replicated genetic association between elevated alanine aminotransferase and genetic variation in the major histocompatibility complex, suggesting a possible autoimmune pathogenesis. Cooper et al. performed a GWAS for the daily maintenance dose of the anticoagulant warfarin [5]. This study replicated previous candidate gene studies that associated common SNPs in VKORC1 and CYP2C9 with large effects on warfarin dose.Quantitative measuresQuantitative measures should be used if possible. For the same sample size, quantitative measures will generally be more powerful than discrete measures. The ADR studies by Kindmark and Cooper are consistent with this idea. Turner et al. conducted a GWAS to identify novel genes influencing diastolic blood pressure response to hydrochlorothiazide, a commonly prescribed diuretic [6]. They discovered a novel gene cluster (encompassing LYZ and YEATS4) that is highly associated with blood pressure response in individuals of both African and European origin.Common events (e.g., treatment outcome or nonresponse) may be more multidetermined and intrinsically less tractable for GWASTwo studies in Table 1 that examined much more common side effects (Inada et al.[7] and Volpi et al.[8]) failed to identify significant associations, supporting the idea that rare side effects may be more likely to resemble ‘monogenetic traits’ and yield highly penetrant variants whereas common events may be akin to common diseases like Type 2 diabetes mellitus. The latter requires very large studies given greater heterogeneity and far smaller genetic effects.Define responders and nonresponders as the extremes of a larger distributionTherapeutic response to a pharmaceutical agent is generally a complex trait that is influenced by numerous genetic and environmental factors. Therefore, assuming adherence, the magnitude of the intended drug response generally shows a continuous phenotypic distribution in outbred populations. Most patients experience a partial therapeutic response, while patients at the tails of the distribution receive either no benefit or a full response. Selective genotyping of individuals at the extremes of the distribution often provides nearly equivalent power to complete genotyping. In addition, the accuracy to which patients are called responders or nonresponders is likely to be highest for patients at the extremes of the distribution. In the antihypertensive GWAS study mentioned above, Turner et al. screened 600 individuals for response to hydrochlorothiazide and selected the 200 ‘best’ and 200 ‘poorest’ responders for genotyping [6]. They discovered a novel gene cluster associated with blood pressure response in individuals of both African and European origin. Two studies in Table 1 that defined responders and nonresponders within the entirety of a small population (Mick et al.[9] and Byun et al.[10]) failed to identify significant associations, supporting the idea that focusing on extreme responders may be the most fruitful approach. These are, of course, early days in the GWAS era for the field of pharmacogenomics and these lessons outlined above may not stand the test of time. However, the take-home message seems to be that the current question of whether or not GWAS will shed light on differential drug response is beginning to look like a ‘yes’ – provided a GWAS is done with care, thoughtfulness and an awareness of the intricacies of the phenotype.Table 1. Summary of published pharmacogenomic GWAS studies.Pharmacological effectGWAS sample (n)Replication sample (n)Genotyping platformGenome-wide significance?Top hit (SNP)p-value (combined)OR (95% CI)Ref.Warfarin maintenance dose181374Illumina 550KYesVKORC1 (rs10871454)4.7 × 10-34–[5]Statin-induced myopathy175∼20,000Illumina 300KYesSLCO1B1 (rs4363657)4.1 × 10-916.9 (4.7–61.1)[2]Elevation of serum ALAT during treatment with ximelagatran20426Perlegen (∼350K)NoMHC-DQA1 (rs17426385)7.3 × 10-84.6 (2.2–9.9)[4]Efficacy of anti-TNF treatment in rheumatoid arthritis890Illumina 300KNoMAFB (rs6028945)2.0 × 10-711.2 (2.3–108.1)[11]Antihypertensive response to thiazide diuretic3890Affymetrix 100KNoChr 12q15 (rs7297610)2.4 × 10-7–[6]Development of jaw osteonecrosis after bisphosphonate in myeloma870Affymetrix 500KNoCYP2C8 (rs1934951)1.1 × 10-612.8 (3.7–43.5)[3]QT prolongation during iloperidone treatment of schizophrenia1830Affymetrix 500KNoNUBPL (rs7142881)1.6 × 10-6–[8]Efficacy of methylphenidate in children with ADHD1870Affymetrix 6.0NoChr 22q13 (rs9627183)3.0 × 10-6–[9]Neuroleptic-induced, treatment-resistant tardive dyskinesia100 (50 TD+)172 (36 TD+)Illumina 100KNoSMYD3 (rs6426327)1.0 × 10-5–[7]Efficacy of interferon-β therapy in multiple sclerosis20681Affymetrix 100K (pooled)NoHAPLN1 (rs4466137)4.0 × 10-3–[10]Etoposide-induced secondary leukemia182 (13 leukemic)0Affymetrix 100KNoMultianalytic study implicates adhesion pathways in secondary leukemias[12] ADHD: Attention deficit–hyperactivity disorder; ALAT: Alanine aminotransferase; CI: Confidence interval; GWAS: Genome-wide association study; OR: Odds ratio; QT: Time interval between the Q and T waves on an electrocardiogram; TD: Tardive dyskinesia; TNF: Tumor necrosis factor.Financial & competing interests disclosureThis work was supported by the NIH Pharmacogenetics Research Network (U01 GM63340). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Papers of special note have been highlighted as: ▪ of interest ▪▪ of considerable interestBibliography1 Altshuler D, Daly MJ, Lander ES: Genetic mapping in human disease. Science322,881–888 (2008).▪ Excellent primer on the current state-of-the-art in complex trait mapping.Crossref, Medline, CAS, Google Scholar2 Link E, Parish S, Armitage J et al.: SLCO1B1 variants and statin-induced myopathy – a genomewide study. N. Engl. J. Med.359,789–799 (2008).▪▪ Demonstrates the ability of genome-wide association studies to map a rare side effect susceptibility variant in a small population.Crossref, Medline, CAS, Google Scholar3 Sarasquete ME, Garcia-Sanz R, Marin L et al.: Bisphosphonate-related osteonecrosis of the jaw is associated with polymorphisms of the cytochrome P450 CYP2C8 in multiple myeloma: a genome-wide single nucleotide polymorphism analysis. Blood112,2709–2712 (2008).Crossref, Medline, CAS, Google Scholar4 Kindmark A, Jawaid A, Harbron CG et al.: Genome-wide pharmacogenetic investigation of a hepatic adverse event without clinical signs of immunopathology suggests an underlying immune pathogenesis. Pharmacogenomics J.8,186–195 (2008).Crossref, Medline, CAS, Google Scholar5 Cooper GM, Johnson JA, Langaee TY et al.: A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood112,1022–1027 (2008).▪▪ Through genome-wide association studies, demonstrated that common SNPs with large effects on warfarin dose are unlikely to be discovered outside of the CYP2C9 and VKORC1 genes.Crossref, Medline, CAS, Google Scholar6 Turner ST, Bailey KR, Fridley BL et al.: Genomic association analysis suggests chromosome 12 locus influencing antihypertensive response to thiazide diuretic. Hypertension52,359–365 (2008).Crossref, Medline, CAS, Google Scholar7 Inada T, Koga M, Ishiguro H et al.: Pathway-based association analysis of genome-wide screening data suggest that genes associated with the γ-aminobutyric acid receptor signaling pathway are involved in neuroleptic-induced, treatment-resistant tardive dyskinesia. Pharmacogenet. Genomics18,317–323 (2008).Crossref, Medline, CAS, Google Scholar8 Volpi S, Heaton C, Mack K et al.: Whole genome association study identifies polymorphisms associated with QT prolongation during iloperidone treatment of schizophrenia. Mol. Psychiatry (2008) (Epub ahead of print).Google Scholar9 Mick E, Neale B, Middleton FA, McGough JJ, Faraone SV: Genome-wide association study of response to methylphenidate in 187 children with attention-deficit/hyperactivity disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet.147B,1412–1418 (2008).Crossref, Medline, CAS, Google Scholar10 Byun E, Caillier SJ, Montalban X et al.: Genome-wide pharmacogenomic analysis of the response to interferon β therapy in multiple sclerosis. Arch. Neurol.65,337–344 (2008).Crossref, Medline, Google Scholar11 Liu C, Batliwalla F, Li W et al.: Genome-wide association scan identifies candidate polymorphisms associated with differential response to anti-TNF treatment in rheumatoid arthritis. Mol. Med.14,575–581 (2008).Crossref, Medline, CAS, Google Scholar12 Hartford C, Yang W, Cheng C et al.: Genome scan implicates adhesion biological pathways in secondary leukemia. Leukemia21,2128–2136 (2007).Crossref, Medline, CAS, Google ScholarFiguresReferencesRelatedDetailsCited ByNo population left behind: Improving paediatric drug safety using informatics and systems biology19 January 2021 | British Journal of Clinical Pharmacology, Vol. 88, No. 4Clinical pharmacogeneticsPlanning and Conducting a Pharmacogenetics Association Study19 June 2021 | Clinical Pharmacology & Therapeutics, Vol. 110, No. 3Genomewide Association Studies in Pharmacogenomics18 July 2021 | Clinical Pharmacology & Therapeutics, Vol. 110, No. 3The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change16 January 2020 | npj Genomic Medicine, Vol. 5, No. 1The role of pharmacogenomics in the personalization of Parkinson's disease treatmentSara Redenšek & Vita Dolžan7 September 2020 | Pharmacogenomics, Vol. 21, No. 14Cardiovascular Pharmacogenomics: Does It Matter If You're Black or White?Annual Review of Pharmacology and Toxicology, Vol. 59, No. 1Personalized MedicinePharmacogenetic Biomarkers to Predict Treatment Response in Multiple Sclerosis: Current and Future PerspectivesMultiple Sclerosis International, Vol. 2017Genome-Wide Association Study Suggested the PTPRD Polymorphisms Were Associated With Weight Gain Effects of Atypical Antipsychotic Medications9 December 2015 | Schizophrenia Bulletin, Vol. 42, No. 3Mechanisms of Vascular Smooth Muscle Contraction and the Basis for Pharmacologic Treatment of Smooth Muscle Disorders1 April 2016 | Pharmacological Reviews, Vol. 68, No. 2Progress towards the integration of pharmacogenomics in practice11 September 2014 | Human Genetics, Vol. 134, No. 5Joint GWAS Analysis: Comparing similar GWAS at different genomic resolutions identifies novel pathway associations with six complex diseasesGenomics Data, Vol. 2High-Throughput Platforms in Drug Metabolism and Transport Pharmacogenetics30 November 2013Pharmacogenomics and Pharmacoepigenomics in Pediatric Medicine3 July 2014Pharmacogenetics of type 2 diabetes mellitus: An example of success in clinical and translational medicineWorld Journal of Translational Medicine, Vol. 3, No. 3Do Tardive Dyskinesia and l-Dopa Induced Dyskinesia Share Common Genetic Risk Factors? An Exploratory Study12 May 2013 | Journal of Molecular Neuroscience, Vol. 51, No. 2State-of-the-Art Technologies to Interrogate Genetic/Genomic Components of Drug Response9 July 2013 | Current Genetic Medicine Reports, Vol. 1, No. 3Genome-wide association studies in pharmacogenomicsPharmacogenetics and Genomics, Vol. 23, No. 8Pharmacogenomics in colorectal cancer: a genome-wide association study to predict toxicity after 5-fluorouracil or FOLFOX administration7 February 2012 | The Pharmacogenomics Journal, Vol. 13, No. 3Ask the Experts: Pharmacogenomics and genome-wide association studiesHakon Hakonarson25 February 2013 | Pharmacogenomics, Vol. 14, No. 4Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D6 February 2013 | PLoS ONE, Vol. 8, No. 2Insights from Genome-Wide Association Studies of Drug ResponseAnnual Review of Pharmacology and Toxicology, Vol. 53, No. 1Designing and Implementing Pharmacogenomics Study3 August 2013Personalized Medicine in Rare Pediatric Neurometabolic Diseases5 January 2012Support for association of HSPG2 with tardive dyskinesia in Caucasian populations2 August 2011 | The Pharmacogenomics Journal, Vol. 12, No. 6Pharmacogenomic strategies in drug safetyDrug Discovery Today: Therapeutic Strategies, Vol. 9, No. 2-3Challenges and opportunities in implementing pharmacogenomics testing in the clinicsJon R Wisler, James W Wisler, Shelly Bansal & Clay B Marsh24 August 2012 | Personalized Medicine, Vol. 9, No. 6Association of the ZFPM2 gene with antipsychotic-induced parkinsonism in schizophrenia patients24 September 2011 | Psychopharmacology, Vol. 220, No. 3Pharmacogenomics in Africa17 March 2012Genetic Variability in Molecular Responses to Chemical Exposure24 April 2012Integrative Analysis of Response to Tamoxifen Treatment in ER-Positive Breast Cancer Using GWAS Information and Transcription Profiling20 February 2012 | Breast Cancer: Basic and Clinical Research, Vol. 6Personalizing medicine with clinical pharmacogeneticsGenetics in Medicine, Vol. 13, No. 12Newly diagnosed epilepsy and pharmacogenomics research: A step in the right direction?Epilepsy & Behavior, Vol. 22, No. 1Personalised medicine in paediatrics: individualising treatment in children with rare neurological diseases1 May 2011 | EPMA Journal, Vol. 2, No. 2Pediatric pharmacogenetic and pharmacogenomic studies: the current state and future perspectives11 November 2010 | European Journal of Clinical Pharmacology, Vol. 67, No. S1Evidence for association of the GLI2 gene with tardive dyskinesia in patients with chronic schizophrenia11 October 2010 | Movement Disorders, Vol. 25, No. 16Pharmacogenomics and bioinformatics: PharmGKBCaroline F Thorn, Teri E Klein and Russ B Altman29 March 2010 | Pharmacogenomics, Vol. 11, No. 4Systems biology of human benzene exposureChemico-Biological Interactions, Vol. 184, No. 1-2Principles of Genetics and Pharmacogenetics26 February 2010Pharmacogenomics and medication overuse headache: when the cure may turn to poisonCherubino Di Lorenzo, Giorgio Di Lorenzo and Filippo M Santorelli20 October 2009 | Pharmacogenomics, Vol. 10, No. 10Genome-wide association study of antipsychotic-induced parkinsonism severity among schizophrenia patients13 August 2009 | Psychopharmacology, Vol. 206, No. 3Pharmacogenetics: What PAs need to understand and whyJournal of the American Academy of Physician Assistants, Vol. 22, No. 7Personalized therapy and pharmacogenomics: future perspectiveGérard Siest, Jean-Brice Marteau & Sophie Visvikis-Siest17 June 2009 | Pharmacogenomics, Vol. 10, No. 6 Vol. 10, No. 2 Follow us on social media for the latest updates Metrics History Published online 10 February 2009 Published in print February 2009 Information© Future Medicine LtdFinancial & competing interests disclosureThis work was supported by the NIH Pharmacogenetics Research Network (U01 GM63340). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download" @default.
• W2157671175 created "2016-06-24" @default.
• W2157671175 creator A5011034688 @default.
• W2157671175 creator A5049973963 @default.
• W2157671175 creator A5061802355 @default.
• W2157671175 date "2009-02-01" @default.
• W2157671175 modified "2023-10-16" @default.
• W2157671175 title "Pharmacogenomic genome-wide association studies: lessons learned thus far" @default.
• W2157671175 cites W1604547439 @default.
• W2157671175 cites W1964966022 @default.
• W2157671175 cites W2002749097 @default.
• W2157671175 cites W2037171637 @default.
• W2157671175 cites W2055128986 @default.
• W2157671175 cites W2056095008 @default.
• W2157671175 cites W2105318826 @default.
• W2157671175 cites W2136078375 @default.
• W2157671175 cites W2150324955 @default.
• W2157671175 cites W2164238938 @default.
• W2157671175 cites W2170984667 @default.
• W2157671175 doi "https://doi.org/10.2217/14622416.10.2.161" @default.
• W2157671175 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3893087" @default.
• W2157671175 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19207016" @default.
• W2157671175 hasPublicationYear "2009" @default.
• W2157671175 type Work @default.
• W2157671175 sameAs 2157671175 @default.
• W2157671175 citedByCount "49" @default.
• W2157671175 countsByYear W21576711752012 @default.
• W2157671175 countsByYear W21576711752013 @default.
• W2157671175 countsByYear W21576711752014 @default.
• W2157671175 countsByYear W21576711752015 @default.
• W2157671175 countsByYear W21576711752016 @default.
• W2157671175 countsByYear W21576711752017 @default.
• W2157671175 countsByYear W21576711752019 @default.
• W2157671175 countsByYear W21576711752020 @default.
• W2157671175 countsByYear W21576711752021 @default.
• W2157671175 countsByYear W21576711752022 @default.
• W2157671175 crossrefType "journal-article" @default.
• W2157671175 hasAuthorship W2157671175A5011034688 @default.
• W2157671175 hasAuthorship W2157671175A5049973963 @default.
• W2157671175 hasAuthorship W2157671175A5061802355 @default.
• W2157671175 hasBestOaLocation W21576711752 @default.
• W2157671175 hasConcept C104317684 @default.
• W2157671175 hasConcept C106208931 @default.
• W2157671175 hasConcept C135763542 @default.
• W2157671175 hasConcept C153209595 @default.
• W2157671175 hasConcept C170734499 @default.
• W2157671175 hasConcept C54355233 @default.
• W2157671175 hasConcept C55775858 @default.
• W2157671175 hasConcept C70721500 @default.
• W2157671175 hasConcept C86803240 @default.
• W2157671175 hasConcept C98274493 @default.
• W2157671175 hasConceptScore W2157671175C104317684 @default.
• W2157671175 hasConceptScore W2157671175C106208931 @default.
• W2157671175 hasConceptScore W2157671175C135763542 @default.
• W2157671175 hasConceptScore W2157671175C153209595 @default.
• W2157671175 hasConceptScore W2157671175C170734499 @default.
• W2157671175 hasConceptScore W2157671175C54355233 @default.
• W2157671175 hasConceptScore W2157671175C55775858 @default.
• W2157671175 hasConceptScore W2157671175C70721500 @default.
• W2157671175 hasConceptScore W2157671175C86803240 @default.
• W2157671175 hasConceptScore W2157671175C98274493 @default.
• W2157671175 hasIssue "2" @default.
• W2157671175 hasLocation W21576711751 @default.
• W2157671175 hasLocation W21576711752 @default.
• W2157671175 hasLocation W21576711753 @default.
• W2157671175 hasLocation W21576711754 @default.
• W2157671175 hasOpenAccess W2157671175 @default.
• W2157671175 hasPrimaryLocation W21576711751 @default.
• W2157671175 hasRelatedWork W2013136572 @default.
• W2157671175 hasRelatedWork W2018617739 @default.
• W2157671175 hasRelatedWork W2029481331 @default.
• W2157671175 hasRelatedWork W2044422899 @default.
• W2157671175 hasRelatedWork W2111564097 @default.
• W2157671175 hasRelatedWork W2168429588 @default.
• W2157671175 hasRelatedWork W2942881285 @default.
• W2157671175 hasRelatedWork W3204659292 @default.
• W2157671175 hasRelatedWork W3217544032 @default.
• W2157671175 hasRelatedWork W2087680831 @default.
• W2157671175 hasVolume "10" @default.
• W2157671175 isParatext "false" @default.
• W2157671175 isRetracted "false" @default.
• W2157671175 magId "2157671175" @default.
• W2157671175 workType "article" @default.