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

Abstract

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.

Genetic heterogeneity of asthma phenotypes identified by a clustering approach

“…However, this consideration of power has to be discussed taking into account the improved phenotypic characterisation accounting for the disease heterogeneity [30]. Large consortia on asthma genetics have been set up based on ''poor'' asthma phenotype definition, which, in the context of a highly heterogeneous disease, may explain part of the missing : p-value for the heterogeneity of the association observed between the asthma phenotypes, assessed using the multinomial model, as described in MORRIS et al [27]; + : because the GABRIEL study (MOFFATT et al [1]) includes participants from European Community Respiratory Health Survey (ECHRS), the Study on Air Pollution and Lung Disease in Adults (SAPALDIA) and the Epidemiological Study on the Genetics and Environment of Asthma (EGEA), the association from the GABRIEL study after excluding these three studies in the meta-analysis is presented.…”

Data compatibility in the addiction sciences: An examination of measure commonality

“…These areas were targeted because we assumed that grant applications within each of these areas would study related topics, and that there would be a tendency for similarity in the constructs and measures. Furthermore, investigators in these areas increasingly recognize the importance of data comparability, interoperability, and integration across multiple studies (Curran and Hussong, 2009), particularly in the area of gene-environment interactions (Bennett et al, 2011; Bierut, 2011; Cornelis et al, 2010; Duncan and Keller, 2011), a research portfolio that is largely housed within the epidemiology domain.…”

Measuring alcohol consumption for genomic meta-analyses of alcohol intake: opportunities and challenges

“…In this regard, it is reassuring that GWAS meta-analyses have proven to be successful at addressing for other complex phenotypes, including behavioral measures (67,68). Furthermore, simulation studies suggest that, even with exposure misclassification, very large studies can succeed at identifying susceptibility loci (69). Importantly, if even a subset of genetic influences is common across each alcohol metric, then a meta-analysis should be able to identify genes contributing to this shared genetic propensity, given a large enough data set.…”

“…Thus, depending on the goals of a study, measures of alcohol consumption can be collected by using food-frequency questionnaires, from medical charts, via self-report in case-control studies of health-related outcomes, and from interviews in studies for addiction and other psychiatric disorders. To illustrate this remarkable variation in the assessment of alcohol consumption, representative alcohol-consumption measures from individual studies of the GENEVA Consortium are shown in Table 1 (46,47).…”