Over at Gene Expression, p-ter has a post up defending the “big genetics” approach, noting that large-scale hypothesis-free genetics studies have consistently yielded important results for follow-up detailed fine-scale studies.
Will Big Genetics eventually swallow the entire field, as some critics of the Human Genome Project argued towards the end of the last millennium? I’d argue that this is unlikely, and that in fact the Big Genetics approach carries within it the seeds of its own constraint.
My reasoning is this: firstly, the sheer size of these projects encourages the emergence of a public data-sharing mentality that now (thankfully) permeates most of the field, becausewith no one group feeling complete ownership of the resulting data there are fewer barriers to the idea of dumping it all online for the benefit of the community as a whole. The free release of data into the research community, like an influx of nutrients into an ecosystem, ultimately results in the increased availability of niches for researchers to exist in. Basically, Big Genetics generates far more data than its participants can ever hope to analyse themselves, and the hefty remainder is fodder for a plethora of small labs exploring small but important facets of the bigger picture.
The vast number of small-scale studies that have relied on the human genome reference sequence or the HapMap is an obvious testament to this process. We are also beginning to see small groups seize on the wealth of data from genome-wide association studies to drive both targeted genetic studies and functional and mechanistic analyses. The increasing hunger of high-impact journals for multi-disciplinary research will ensure that the drive for collaboration is always there, but groups won’t need to be absorbed within these massive consortia in order to take advantage of their data output.
My guess is that – contrary to the Big critics – the human genetics ecosystem will continue to fluctuate around an equilibrium point marking a fairly comfortable balance between Big and Small Genetics. However, the crucial symbol in the equation is the free release of data, meaning anything that interferes with open data access is a threat to the research community as a whole – so we need to be wary both of an excessive focus on commercialisation within academia, and of well-meaning but excessive attempts to control the flow of data, like this.