Confounding, ascertainment bias, and the blind quest for a genetic 'fountain of youth'

Ann Med. 2003;35(7):532-44. doi: 10.1080/07853890310015181.


Many promises have been made about the impact of the Human Genome Project on clinical practice and public health, yet despite massively funded efforts over the past decade, little headway has been made in elucidating the specific genetic factors which have major impact on the risk of developing common complex traits. There are two fundamental reasons for this abject failure as follows: 1) studies have been inadequately designed to identify such genetic risk factors; 2) the genetic factors that do exist are individually of small marginal importance, and are characterized by extensive heterogeneity. If 2) is the truth, there is little we can do about it, so we emphasize the importance of 1) in this article, while recognizing that 2) probably is not far from the truth. Genetic studies, in contrast to epidemiological studies, use confounding and ascertainment bias to help identify weak etiologic signal due to genes, since gene mapping is fundamentally a hypothesis-free science. This strategy makes it possible to identify genetic risk factors, but makes it impossible to quantify the size of their effect on risk. Classical epidemiological study designs are of minimal value for gene identification, but may be of use in estimation of the effect size of genetic risk factors once they are identified in more appropriately designed genetic studies. However, if the effects are so weak that we need this strong, systematic ascertainment bias to find them, their relevance to public health may be of questionable immediate value, raising many questions about the rhetoric and promises being made to the public as justification for 'big science' approaches to dissecting the hypothetical role of genes in complex traits.

Publication types

  • Research Support, U.S. Gov't, P.H.S.
  • Review

MeSH terms

  • Animals
  • Environmental Exposure
  • Epidemiology
  • Genetic Linkage
  • Genetic Research
  • Genetic Variation
  • Genetics*
  • Genetics, Medical
  • Humans
  • Linkage Disequilibrium
  • Models, Animal
  • Molecular Biology
  • Public Health
  • Stochastic Processes