Click the star to add/remove an item to/from your individual schedule.
You need to be logged in to avail of this functionality.

Accepted Paper:

Containing complexity: networks of expertise and the emergence of genetic epidemiology, 1900-1990  
Carolina Mayes (University of Edinburgh) Rhodri Leng (University of Edinburgh)

Send message to Authors

Short abstract:

Using bibliometric analysis, we demonstrate how the subfield of genetic epidemiology (GE) emerged through the incorporation of epidemiological expertise into a network formerly dominated by statistical geneticists. We suggest that the formalization of GE contained complex causality within genetics.

Long abstract:

This paper takes the contemporary problem of complexity in human genomic research backwards in time, to describe how earlier frustrations in genetic disease research were contained into a new subfield, genetic epidemiology. We draw on bibliometric and citation network analysis (Leng and Leng 2021) to recreate a publication history of genetic epidemiology, from the early 20th century up to the field’s formalization around its flagship journal in the mid-1980s, just prior to the Human Genome Project’s launch. Using Web of Science indexing of 2,625 papers, we generate a network map of epidemiological studies of genetic factors and genetic studies of familial and population patterns of disease. Through this analysis, we trace how formerly distant networks of epidemiological and genetic expertise were drawn together in the 1960s through shared interest in complex or chronic disease causality. We identify how epidemiological genetics emerged alongside medical genetics but also deviated from it, as researchers struggled to isolate distinct phenotypes for complex disease and argued about the utility of classical Mendelian models of transmission in potentially non-Mendelian disease research. We suggest that the gradual incorporation of epidemiological expertise enabled researchers to maintain a viable research program on genetic contributions to multifactorial disease, providing a launching pad for the new method of genome-wide association studies. Our analysis demonstrates how an expanded network of expertise (Eyal 2013) allowed researchers to adapt to and manage etiological complexity, containing debates about the validity of particular methods and questions and redirecting intellectual interest to new ideas.

Traditional Open Panel P185
What is to be done? Data infrastructures and doable problems in epidemiology, biomedicine, and beyond
  Session 1 Friday 19 July, 2024, -