Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome

Abstract

The exposome, defined as the totality of an individual's exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal and behavioral determinants (the behavome). Genetic GISc poses three key needs: First, a mathematical foundation for emergent theory; Second, process-based models that bridge biological and geographic scales; Third, biologically plausible estimates of space-time disease lags. Compartmental models are a possible solution; this article develops two models using pancreatic cancer as an exemplar. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages by diagnostic criteria. These provide empirical estimates of the distribution of latencies in cellular states and disease stages, and maps of the burden of yet to be diagnosed disease. This approach links our emerging knowledge of genomics to cancer progression at the cellular level, to individuals and their cancer stage at diagnosis, to geographic distributions of cancer in extant populations. These methodological developments and exemplar provide the basis for a new synthesis in health geography: genetic geographic information science.

Keywords: Cancer epidemiology; dynamical systems; genetic GISc; health geography; space-time.

Figure 1

Schematic representation of genetic geographic information science (Genetic GIS). The three primary determinants of health, both in terms of illness and well being, are (1) an individual’s biology which may quantified as their “Genome +”, comprised of their genome (genetic composition), regulome (which controls gene expression), proteome (their compliment of amino acids and proteins) and metabalome (the basis of metabolism and homeostasis). (2) The environments they experience, which may be quantified as the exposome, is defined as the totality of exposures over the life course (Wild 2005a). (3) The totality of an individual’s health behaviors over the life course, which may be quantified as the behavome, mediate the exposome and interactions between the exposome and the genome +. These determinants of human health act through place, defined as the geographic, environmental, social and societal milieus experienced over a person’s life course. This synthesis is referred to as genetic geographic science, or genetic GIS.

Figure 2

Steps in dynamic geographic systems analysis (left) and specific application to cancer using knowledge of residential history to budget excess risk (right).

Figure 3

Schematic of the evolution of pancreatic cancer. Normal pancreatic duct epithelial cells undergo mutation events to become an initiated tumor cell. Additional mutations and clonal expansions lead eventually to a founder cell of the index pancreatic cancer clone. These produce subclones with metastatic capacity, eventually leading to dissemination to distant organs such as the liver. Times shown are the empirical residence times in each system state. Adapted from Yachida, Jones et al. (2010).

Figure 4

Model of pancreatic cancer carcinogenesis.

Figure 5

Outflow connected n compartment system useful for solving for the probability density function and cumulative distribution function of residence times.

Figure 6

Stage-based model of pancreatic cancer. Here the compartment sizes are number of patients with early (q₆) and late stage cancers (q₇) prior to diagnosis, and the number diagnosed (q₈).

Figure 7

Choropleth maps of pancreatic cancer cases in southeast Michigan, 1985–2005: (A) incident cases; (B) stage-known cases; (C) silent (yet to be diagnosed) cases.

Figure 8

Frequency histogram of silent (yet to be diagnosed) pancreatic cancer cases in the greater Detroit metropolitan area.

Figure 9

i^th compartment of a compartmental system with flows to (F_0i(q,t)) and from (I_i(t)) outside the system. Flows to and from other compartments are F_ji(q,t) and F_ij(q,t), respectively. The size of the i^th compartment at time t is q_i(t). Source: Jacquez (Jacquez 1996).

Figure 10

PanIN subsystem model for estimation of distributions of residence times. Original subsystem model (left); simplified model used for calculation of distributions of residence times (right).