Below an extract from a recent very interesting paper on gene–environment interactions research:

Ackerman, S. L., et al. (2015). “Accounting for Complexity: Gene–environment Interaction Research and the Moral Economy of Quantification.” Science, Technology & Human Values. http://sth.sagepub.com/content/early/2015/07/10/0162243915595462.abstract

…the lure of genetic determinism is loosening its grip as new understandings of developmental and etiological complexity undermine ‘‘the preordained genetic
body’’ (Lock 2005, S49) and displace the gene as the prime mover of health
and illness. Emerging knowledge about gene regulation, epigenetics, and
gene–environment interactions (GEIs) contributes to a growing recognition
among life scientists that disease arises through an interplay of genetic variations
and social, political, economic, and environmental phenomena
(Landecker and Panofsky 2012). […] Epidemiology is one area of scientific
inquiry that has been strongly influenced by genomics. […] as disease causality is increasingly
understood to arise through an entanglement of genetic and nongenetic variables,
many epidemiologists now incorporate molecular concepts and technologies
in their studies of common diseases. […] This paper examines GEI research […] GEI studies are premised on the idea that genetic differences mediate physiological responses
to different environments, such as toxins, diet, stress, or socioeconomic inequality—and, reciprocally, that environmental exposures can regulate development or gene expression. […]

We explore GEI researchers’ striving for molecular precision and
attempts to ‘‘harmonize’’ themselves, and their data, as both an enactment
of the virtues of quantification and a politics of standardization. […]

molecular measures of hard-to-quantify risk factors such as diet are increasingly associated with scientific progress and rigor, and as such they are replacing questionnaires and other procedures for gathering data based on study participants’ recollections or reports. This shift is guided both by technological developments and by members of scientific collectives who influence funding and publication decisions, […]

molecular measures are less prone to variations in interpretation and measurement (‘‘error’’)
and are therefore more precise and more faithful contributors to statistical
significance. […]

Of particular concern among many of our participants was the exchange of real-world complexity for statistical significance through precise measurement and harmonizable data.

 

Citing Daston 1995, the paper offers a different look at the word ‘precision’ (as in precision measurement):

precision aims for clarity and intelligibility, and ‘‘by itself, stipulates
nothing about whether and how those concepts match the world’’
(Daston 1995, 8)

Daston, Lorraine. 1995. ‘‘The Moral Economy of Science.’’ Osiris 10:2-24.