Reporting of human genome epidemiology (HuGE) association studies: an empirical assessment

• Published in: BMC medical research methodology Vol. 8; no. 1; p. 31
• Main Authors: Yesupriya, Ajay, Evangelou, Evangelos, Kavvoura, Fotini K, Patsopoulos, Nikolaos A, Clyne, Melinda, Walsh, Matthew C, Lin, Bruce K, Yu, Wei, Gwinn, Marta, Ioannidis, John P A, Khoury, Muin J
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.05.2008; BioMed Central; BMC
• Subjects: Analysis; Bias; Empirical Research; Epidemiologic Research Design; Epidemiology; Genetics, Medical; Genome, Human; Genotype; Health aspects; Human genome; Humans; Methods; Molecular Epidemiology; United States
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/1471-2288-8-31

Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies. 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 2001-2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001-2003. 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.