%0 Journal Article
%T Reporting of Human Genome Epidemiology (HuGE) association studies: An empirical assessment
%A Ajay Yesupriya
%A Evangelos Evangelou
%A Fotini K Kavvoura
%A Nikolaos A Patsopoulos
%A Melinda Clyne
%A Matthew C Walsh
%A Bruce K Lin
%A Wei Yu
%A Marta Gwinn
%A John PA Ioannidis
%A Muin J Khoury
%J BMC Medical Research Methodology
%D 2008
%I BioMed Central
%R 10.1186/1471-2288-8-31
%X Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001¨C2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001¨C2003.During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.Human genome epidemiology (HuGE) is a rapidly emerging scientific field that examines the influence of genomic variation on human health [1-4]. Although a large and rapidly increasing number of studies have investigated the associations between genetic variants and the risks of common diseases through observational epidemiology, few significant associations have been shown to be reproducible in multiple studies [5,6]. Transparent reporting of the study populations, methods of data collection, analytic methods, and study inferences may help readers better identify issues that can affect the reproducibility of genetic association studies. Here, we conduct a detailed evaluation of reporting practices for HuGE association studies.In 2001, the Human Gen
%U http://www.biomedcentral.com/1471-2288/8/31