Genetic analysis of asthma exacerbations.

BACKGROUND: Identifying genetic markers of susceptibility to exacerbations may improve patient management, decrease morbidity, and lead to drug development. OBJECTIVES: To assess whether genetic markers associated with severe asthma exacerbations in previous reports are associated with less severe events that do not require intensive care and intubation and to identify additional markers in candidate genes and throughout the genome. METHODS: A total of 199 patients and 502 controls (individuals without an exacerbation) were identified from 4 clinical trials. We genotyped 51 markers from 17 genes previously reported to be associated with exacerbations; a whole genome scan was used to identify additional markers. Admixture analysis was conducted to characterize the presence of ancestral groups. The genetic marker effects were assessed by logistic regression for each study followed by a meta-analysis. RESULTS: Several coding variants in the IL4R gene had a genetic effect across 3 studies, including rs1805011 in IL4R (P < .0006). In addition, 3 markers in the IFNB1 gene showed evidence of association (P < .002) but only in the study with African Americans. Because these markers did not meet the prespecified multiplicity-adjusted significance level of P = .0002, we were unable to confirm previously published results for less severe events. The whole genome scan identified genes related to mast cell mediator release. The admixture analysis suggests that ancestry was best characterized by the presence of 3 ancestral groups. CONCLUSION: We were unable to confirm previously reported associations of genetic markers with asthma exacerbations. Although, in general, the patients studied had less severe asthma than patients in earlier reports, these results suggest involvement of similar pathways. TRIAL REGISTRATION: clinicaltrials.gov Identifiers: NTC00452699, NCT00452348, NTC00102765, NCT00843193.

Predictive models of choroidal neovascularization and geographic atrophy incidence applied to clinical trial design.

PURPOSE: To develop comprehensive predictive models for choroidal neovascularization (CNV) and geographic atrophy (GA) incidence within 3 years that can be applied realistically to clinical practice. DESIGN: Retrospective evaluation of data from a longitudinal study to develop and validate predictive models of CNV and GA. METHODS: The predictive performance of clinical, environmental, demographic, and genetic risk factors was explored in regression models, using data from both eyes of 2011 subjects from the Age-Related Eye Disease Study (AREDS). The performance of predictive models was compared using 10-fold cross-validated receiver operating characteristic curves in the training data, followed by comparisons in an independent validation dataset (1410 AREDS subjects). Bayesian trial simulations were used to compare the usefulness of predictive models to screen patients for inclusion in prevention clinical trials. RESULTS: Logistic regression models that included clinical, demographic, and environmental factors had better predictive performance for 3-year CNV and GA incidence (area under the receiver operating characteristic curve of 0.87 and 0.89, respectively), compared with simple clinical criteria (AREDS simplified severity scale). Although genetic markers were associated significantly with 3-year CNV (CFH: Y402H; ARMS2: A69S) and GA incidence (CFH: Y402H), the inclusion of genetic factors in the models provided only marginal improvements in predictive performance. CONCLUSIONS: The logistic regression models combine good predictive performance with greater flexibility to optimize clinical trial design compared with simple clinical models (AREDS simplified severity scale). The benefit of including genetic factors to screen patients for recruitment to CNV prevention studies is marginal and is dependent on individual clinical trial economics.

Candidate single-nucleotide polymorphisms from a genomewide association study of Alzheimer disease.

OBJECTIVE: To identify single-nucleotide polymorphisms (SNPs) associated with risk and age at onset of Alzheimer disease (AD) in a genomewide association study of 469 438 SNPs. DESIGN: Case-control study with replication. SETTING: Memory referral clinics in Canada and the United Kingdom. PARTICIPANTS: The hypothesis-generating data set consisted of 753 individuals with AD by National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer's Disease and Related Disorders Association criteria recruited from 9 memory referral clinics in Canada and 736 ethnically matched control subjects; control subjects were recruited from nonbiological relatives, friends, or spouses of the patients and did not exhibit cognitive impairment by history or cognitive testing. The follow-up data set consisted of 418 AD cases and 249 nondemented control cases from the United Kingdom Medical Research Council Genetic Resource for Late-Onset AD recruited from clinics at Cardiff University, Cardiff, Wales, and King's College London, London, England. MAIN OUTCOME MEASURES: Odds ratios and 95% confidence intervals for association of SNPs with AD by logistic regression adjusted for age, sex, education, study site, and French Canadian ancestry (for the Canadian data set). Hazard ratios and 95% confidence intervals from Cox proportional hazards regression for age at onset with similar covariate adjustments. RESULTS: Unadjusted, SNP RS4420638 within APOC1 was strongly associated with AD due entirely to linkage disequilibrium with APOE. In the multivariable adjusted analyses, 3 SNPs within the top 120 by P value in the logistic analysis and 1 in the Cox analysis of the Canadian data set provided additional evidence for association at P< .05 within the United Kingdom Medical Research Council data set: RS7019241 (GOLPH2), RS10868366 (GOLPH2), RS9886784 (chromosome 9), and RS10519262 (intergenic between ATP8B4 and SLC27A2). CONCLUSIONS: Our genomewide association analysis again identified the APOE linkage disequilibrium region as the strongest genetic risk factor for AD. This could be a consequence of the coevolution of more than 1 susceptibility allele, such as APOC1, in this region. We also provide new evidence for additional candidate genetic risk factors for AD that can be tested in further studies.