HIV Infection and Carotid Artery Intima-media Thickness: Pooled Analyses Across 5 Cohorts of the NHLBI HIV-CVD Collaborative.

BACKGROUND: Age and human immunodeficiency virus (HIV) treatment may affect the association of HIV infection with atherosclerosis. METHODS: We used identical carotid artery B-mode ultrasonographic methods in 5 cohorts participating in the National Heart, Lung, and Blood Institute HIV-CVD Collaborative to measure intima-media thickness of the right far wall of the common carotid artery (CCA-IMT) and carotid artery bifurcation (BIF-IMT) between 2010 and 2013. Participants aged 6-75 years were either HIV infected or uninfected. Linear regression assessed associations of CCA-IMT and BIF-IMT with HIV infection and cardiovascular disease risk factors, within age and HIV treatment groups. Adjustment variables included sex, race/ethnicity, smoking, height, weight, and use of antihypertensive and lipid-lowering drugs. RESULTS: We studied 867 HIV-infected and 338 HIV-uninfected male and 696 HIV-infected and 246 HIV-uninfected female participants. Among both middle-aged (30-49 years) and older adults (50-75 years), HIV-infected participants had CCA-IMT and BIF-IMT values that were similar to or lower than those in HIV-uninfected participants. In contrast, among those aged 6-29 years, HIV infection was associated with higher CCA-IMT and BIF-IMT values. Among HIV-infected participants, associations of higher systolic blood pressure and lower high-density lipoprotein cholesterol with Carotid artery intima-media thickness strengthened with age. CONCLUSIONS: The effects of HIV on carotid artery structure may differ across the lifespan, with traditional determinants of cardiovascular disease burden playing a larger role and HIV playing a lesser role in older adults than in young adults and children.

Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption.

We report the first genome-wide association study of habitual caffeine intake. We included 47,341 individuals of European descent based on five population-based studies within the United States. In a meta-analysis adjusted for age, sex, smoking, and eigenvectors of population variation, two loci achieved genome-wide significance: 7p21 (P = 2.4 × 10(-19)), near AHR, and 15q24 (P = 5.2 × 10(-14)), between CYP1A1 and CYP1A2. Both the AHR and CYP1A2 genes are biologically plausible candidates as CYP1A2 metabolizes caffeine and AHR regulates CYP1A2.

Phenotype harmonization and cross-study collaboration in GWAS consortia: the GENEVA experience.

Genome-wide association study (GWAS) consortia and collaborations formed to detect genetic loci for common phenotypes or investigate gene-environment (G*E) interactions are increasingly common. While these consortia effectively increase sample size, phenotype heterogeneity across studies represents a major obstacle that limits successful identification of these associations. Investigators are faced with the challenge of how to harmonize previously collected phenotype data obtained using different data collection instruments which cover topics in varying degrees of detail and over diverse time frames. This process has not been described in detail. We describe here some of the strategies and pitfalls associated with combining phenotype data from varying studies. Using the Gene Environment Association Studies (GENEVA) multi-site GWAS consortium as an example, this paper provides an illustration to guide GWAS consortia through the process of phenotype harmonization and describes key issues that arise when sharing data across disparate studies. GENEVA is unusual in the diversity of disease endpoints and so the issues it faces as its participating studies share data will be informative for many collaborations. Phenotype harmonization requires identifying common phenotypes, determining the feasibility of cross-study analysis for each, preparing common definitions, and applying appropriate algorithms. Other issues to be considered include genotyping timeframes, coordination of parallel efforts by other collaborative groups, analytic approaches, and imputation of genotype data. GENEVA's harmonization efforts and policy of promoting data sharing and collaboration, not only within GENEVA but also with outside collaborations, can provide important guidance to ongoing and new consortia.

The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions.

Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene-trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

Incidental genetic findings in randomized clinical trials: recommendations from the Genomics and Randomized Trials Network (GARNET).

Recommendations and guidance on how to handle the return of genetic results to patients have offered limited insight into how to approach incidental genetic findings in the context of clinical trials. This paper provides the Genomics and Randomized Trials Network (GARNET) recommendations on incidental genetic findings in the context of clinical trials, and discusses the ethical and practical issues considered in formulating our recommendations. There are arguments in support of as well as against returning incidental genetic findings in clinical trials. For instance, reporting incidental findings in clinical trials may improve the investigator-participant relationship and the satisfaction of participation, but it may also blur the line between clinical care and research. The issues of whether and how to return incidental genetic findings, including the costs of doing so, should be considered when developing clinical trial protocols. Once decided, plans related to sharing individual results from the aim(s) of the trial, as well as incidental findings, should be discussed explicitly in the consent form. Institutional Review Boards (IRBs) and other study-specific governing bodies should be part of the decision as to if, when, and how to return incidental findings, including when plans in this regard are being reconsidered.