title: On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies.
abstract: Emerging technologies are allowing researchers to study hundreds of thousands of genetic variants simultaneously as risk factors for common complex diseases. Both theoretical considerations and empirical evidence suggest that specific genetic variants causally associated with common diseases will have small effects (risk ratios mostly <2.0). However, the combination of even a few small effects (e.g. effects of fewer than 20 common genetic variants) could account for a sizeable population attributable fraction of common diseases and shed important light on disease pathogenesis and environmental determinants. Nevertheless, the inauguration of genome-wide association studies only magnifies the challenge of differentiating between the expected, true weak associations from the numerous spurious effects caused by misclassification, confounding and significance-chasing biases. Standards are urgently needed for presenting and interpreting cumulative evidence on gene-disease associations, especially for consistent but weak associations. Criteria for synthesis of the evidence should include sound methods for study conduct and analysis, biological plausibility, experimental evidence and adequate replication in large-scale, collaborative studies. Efforts by the Human Genome Epidemiology Network (HuGENet) are currently ongoing to streamline and operationalize these criteria for data on genetic associations with common diseases.
authors.affiliation: National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA. mkhoury@cdc.gov
authors.forename: Muin J
authors.initials: MJ
authors.lastname: Khoury
authors.forename: Julian
authors.initials: J
authors.lastname: Little
authors.forename: Marta
authors.initials: M
authors.lastname: Gwinn
authors.forename: John P  A
authors.initials: JP
authors.lastname: Ioannidis
mesh_terms: D015331:Cohort Studies; D004812:Epidemiologic Methods; D005260:Female; D030342:Genetic Diseases, Inborn / Q000453:epidemiology / Q000235:genetics*; D023281:Genomics / Q000379:methods* / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D008297:Male; D016017:Odds Ratio