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The most common side effect of angiotensin-converting enzyme inhibitor (ACEi) drugs is cough. We conducted a genome-wide association study (GWAS) of ACEi-induced cough among 7080 subjects of diverse ancestries in the Electronic Medical Records and Genomics (eMERGE) network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1595 cases and 5485 controls) identified associations on chromosome 4 in an intron of KCNIP4. The strongest association was at rs145489027 (minor allele frequency=0.33, odds ratio (OR)=1.3 (95% confidence interval (CI): 1.2-1.4), P=1.0 × 10(-8)). Replication for six single-nucleotide polymorphisms (SNPs) in KCNIP4 was tested in a second eMERGE population (n=926) and in the Genetics of Diabetes Audit and Research in Tayside, Scotland (GoDARTS) cohort (n=4309). Replication was observed at rs7675300 (OR=1.32 (1.01-1.70), P=0.04) in eMERGE and at rs16870989 and rs1495509 (OR=1.15 (1.01-1.30), P=0.03 for both) in GoDARTS. The combined association at rs1495509 was significant (OR=1.23 (1.15-1.32), P=1.9 × 10(-9)). These results indicate that SNPs in KCNIP4 may modulate ACEi-induced cough risk.
Pubmed ID: 26169577
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Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.
View all literature mentionsCollaborative environment of building and validating electronic phenotype algorithms using electronic medical records (EMRs) and natural language processing (NLP) for use in genome-wide association studies (GWAS). On this site you can: View existing algorithms, Enter or create new algorithms, Collaborate with others to create or review algorithms, View implementation details for existing algorithms. The Electronic Medical Records and Genomics Network (eMERGE) has investigated whether data captured through routine clinical care using electronic medical records (EMRs) can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format; in addition, natural language processing has also been shown to improve case identification rates. PheKB is an outgrowth of that validation effort. Phenotype algorithms can be viewed by data modalities or methods used: CPT codes, ICD 10 codes, ICD 9 codes, Laboratories, Medications, Vital Signs, Natural Language Processing Algorithms can also be viewed by: * Implementation results (positive predictive value, sensitivity, publications) * Institution * Work Group
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software tool for meta analysis of whole genome association data.
View all literature mentionsHaploReg is a tool for exploring annotations of the noncoding genome at variants on haplotype blocks, such as candidate regulatory SNPs at disease-associated loci. Using linkage disequilibrium (LD) information from the 1000 Genomes Project, linked SNPs and small indels can be visualized along with their predicted chromatin state in nine cell types, conservation across mammals, and their effect on regulatory motifs. HaploReg is designed for researchers developing mechanistic hypotheses of the impact of non-coding variants on clinical phenotypes and normal variation.
View all literature mentionsSoftware program for the analysis of single SNP association in genome-wide studies. The tests implemented can cater for binary (case-control) and quantitative phenotypes, can condition upon an arbitrary set of covariates and properly account for the uncertainty in genotypes. The program is designed to work seamlessly with the output of both the genotype calling program CHIAMO, the genotype imputation program IMPUTE and the program GTOOL. This program was used in the analysis of the 7 genome-wide association studies carried out by the Wellcome Trust Case-Control Consortium (WTCCC). (entry from Genetic Analysis Software)
View all literature mentionsA computer program for phasing observed genotypes and imputing missing genotypes.
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