It is said that if one has a hammer, everything looks like a nail. But it may also be said that if one has a hammer one finds unexpected uses for the concentrated force it provides (ranging from combining with an awl to make holes in one’s belt to creating a means of murder in who done it novels). In the same spirit, technologies to identify DNA sequences can be played around with to expose unexpected uses. Initially, the technologies were used to transfer sequences from complex organisms into bacteria. Soon after, researchers were comparing the sequences within species to see how much variation existed in natural populations, determining whether that variation was of recent origin or had been maintained over a longer evolutionary period, tracing relatedness and genealogies of taxa, etc. etc. Over the last decade, biobanks have been funded — enormous databases of human genetic variation with accompanying information about disease incidence, with the hope of finding associations between genes and diseases.
“Playing field is not level”
John Frank ( 2005), Scientific Director of the Institute of Population and Public Health of the Canadian Institutes for Health Research, has… ask[ed] what data needs to be collected over the life course of individuals so that researchers in say, thirty years, have the information needed to identify the key risk factors and interactions that account for variation in disease incidence and differential age of onset in a population, and for changing patterns for diseases over time. He assumes that ‘diseases and conditions of later life occur in some and not others because of intense interactions between particular genetic constitutions and particular sequence of social and physical environments.’ There is, however, an uneven playing field. Genetic samples are cheap to collect and store and need to be collected only once in a lifetime. Environmental exposures vary over time so that ‘new samples are needed whenever exposure changes, are difficult to store, and are ‘getting costlier (as awareness of chemical/physical/ biological complexity increases).’ Some epidemiologists have secured resources to follow small chorts through time and collect a rich array of data on the individuals (e.g., The Southampton Women’s Survey [Inskip et al. 2006]), but the major investments are being made in collecting primarily genetic and disease data for large samples (e.g., the UK Biobank). Epidemiologists such as Frank have warned that analyses of such data will depend on crude estimates of environmental factors and be subject to large errors, uncertainties, and non-replicated findings about genetic influences. In the absence of longituidinal data on environmental exposures, biomedicine has almost no option but to emphasize the effects of genetic factors (but see Davey-Smith and Ebrahim 2007).
Extracted from previous post, in turn extracted from P. Taylor, “Infrastructure and Scaffolding: Interpretation and Change of Research Involving Human Genetic Information,” Science as Culture, 18(4):435-459, 2009
(Introduction to this series of posts)