RESUMO
The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2C19 gene. CYP2C19 genetic variation impacts the metabolism of many drugs and has been associated with both efficacy and safety issues for several commonly prescribed medications. This GeneFocus provides a comprehensive overview and summary of CYP2C19 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase and the Clinical Pharmacogenetics Implementation Consortium (CPIC).
Assuntos
Citocromo P-450 CYP2C19/genética , Alelos , Variação Genética/genética , Genótipo , Haplótipos/genética , Humanos , Bases de Conhecimento , Farmacogenética/métodosRESUMO
STUDY DESIGN: A case-control genome-wide association study (GWAS) on spondylosis. OBJECTIVE: Leveraging Geisinger's MyCode initiative's multimodal dataset, we aimed to identify genetic associations with degenerative spine disease. SUMMARY OF BACKGROUND DATA: Degenerative spine conditions are a leading cause of global disability; however, the genetic underpinnings of these conditions remain under-investigated. Previous studies using candidate-gene approach suggest a genetic risk for degenerative spine conditions, but large-scale GWASs are lacking. METHODS: We identified 4434 patients with a diagnosis of spondylosis using ICD diagnosis codes with genotype data available. We identified a population-based control of 12,522 patients who did not have any diagnosis for osteoarthritis. A linear-mix, additive genetic model was employed to perform the genetic association tests adjusting for age, sex, and genetic principal components to account for the population structure and relatedness. Gene-based association tests were performed and heritability and genetic correlations with other traits were investigated. RESULTS: We identified a genome-wide significant locus at rs12190551 (odds ratioâ=â1.034, 95% confidence interval 1.022-1.046, Pâ=â8.5 × 10-9, minor allele frequency = 36.9%) located in the intron of BMP6. Additionally, NIPAL1 and CNGA1 achieved Bonferroni significance in the gene-based association tests. The estimated heritability was 7.19%. Furthermore, significant genetic correlations with pain, depression, lumbar spine bone mineral density, and osteoarthritis were identified. CONCLUSION: We demonstrated the use of a massive database of genotypes combined with electronic health record data to identify a novel and significant association spondylosis. We also identified significant genetic correlations with pain, depression, bone mineral density, and osteoarthritis, suggesting shared genetic etiology and molecular pathways with these phenotypes.Level of Evidence: N/A.
Assuntos
Proteína Morfogenética Óssea 6/genética , Proteínas de Transporte de Cátions/genética , Canais de Cátion Regulados por Nucleotídeos Cíclicos/genética , Espondilose , Estudos de Casos e Controles , Feminino , Predisposição Genética para Doença/genética , Estudo de Associação Genômica Ampla , Humanos , Masculino , Espondilose/epidemiologia , Espondilose/genéticaRESUMO
BACKGROUND: Polycystic ovary syndrome is the most common endocrine disorder affecting women of reproductive age. A number of criteria have been developed for clinical diagnosis of polycystic ovary syndrome, with the Rotterdam criteria being the most inclusive. Evidence suggests that polycystic ovary syndrome is significantly heritable, and previous studies have identified genetic variants associated with polycystic ovary syndrome diagnosed using different criteria. The widely adopted electronic health record system provides an opportunity to identify patients with polycystic ovary syndrome using the Rotterdam criteria for genetic studies. OBJECTIVE: To identify novel associated genetic variants under the same phenotype definition, we extracted polycystic ovary syndrome cases and unaffected controls based on the Rotterdam criteria from the electronic health records and performed a discovery-validation genome-wide association study. STUDY DESIGN: We developed a polycystic ovary syndrome phenotyping algorithm on the basis of the Rotterdam criteria and applied it to 3 electronic health record-linked biobanks to identify cases and controls for genetic study. In the discovery phase, we performed an individual genome-wide association study using the Geisinger MyCode and the Electronic Medical Records and Genomics cohorts, which were then meta-analyzed. We attempted validation of the significant association loci (P<1×10-6) in the BioVU cohort. All association analyses used logistic regression, assuming an additive genetic model, and adjusted for principal components to control for population stratification. An inverse-variance fixed-effect model was adopted for meta-analysis. In addition, we examined the top variants to evaluate their associations with each criterion in the phenotyping algorithm. We used the STRING database to characterize protein-protein interaction network. RESULTS: Using the same algorithm based on the Rotterdam criteria, we identified 2995 patients with polycystic ovary syndrome and 53,599 population controls in total (2742 cases and 51,438 controls from the discovery phase; 253 cases and 2161 controls in the validation phase). We identified 1 novel genome-wide significant variant rs17186366 (odds ratio [OR]=1.37 [1.23, 1.54], P=2.8×10-8) located near SOD2. In addition, 2 loci with suggestive association were also identified: rs113168128 (OR=1.72 [1.42, 2.10], P=5.2×10-8), an intronic variant of ERBB4 that is independent from the previously published variants, and rs144248326 (OR=2.13 [1.52, 2.86], P=8.45×10-7), a novel intronic variant in WWTR1. In the further association tests of the top 3 single-nucleotide polymorphisms with each criterion in the polycystic ovary syndrome algorithm, we found that rs17186366 (SOD2) was associated with polycystic ovaries and hyperandrogenism, whereas rs11316812 (ERBB4) and rs144248326 (WWTR1) were mainly associated with oligomenorrhea or infertility. We also validated the previously reported association with DENND1A1. Using the STRING database to characterize protein-protein interactions, we found both ERBB4 and WWTR1 can interact with YAP1, which has been previously associated with polycystic ovary syndrome. CONCLUSION: Through a discovery-validation genome-wide association study on polycystic ovary syndrome identified from electronic health records using an algorithm based on Rotterdam criteria, we identified and validated a novel genome-wide significant association with a variant near SOD2. We also identified a novel independent variant within ERBB4 and a suggestive association with WWTR1. With previously identified polycystic ovary syndrome gene YAP1, the ERBB4-YAP1-WWTR1 network suggests involvement of the epidermal growth factor receptor and the Hippo pathway in the multifactorial etiology of polycystic ovary syndrome.