Genetics in population health science: strategies and opportunities

Abstract

Translational research is needed to leverage discoveries from the frontiers of genome science to improve public health. So far, public health researchers have largely ignored genetic discoveries, and geneticists have ignored important aspects of population health science. This mutual neglect should end. In this article, we discuss 3 areas where public health researchers can help to advance translation: (1) risk assessment: investigate genetic profiles as components in composite risk assessments; (2) targeted intervention: conduct life-course longitudinal studies to understand when genetic risks manifest in development and whether intervention during sensitive periods can have lasting effects; and (3) improved understanding of environmental causation: collaborate with geneticists on gene-environment interaction research. We illustrate with examples from our own research on obesity and smoking.

FIGURE 1—

Risks for obesity and smoking associated with genetic and family history-based risk scores: Dunedin Multidisciplinary Health and Development Study, 1972–2013. Note. Effect sizes are shown as increments in the relative risk of the outcome associated with a 1 SD increase in the risk score. The obesity family history risk score was based on parental body mass index. The smoking family history risk score was based on the smoking behavior of cohort members’ parents, siblings, and grandparents. Error bars reflect 95% confidence intervals. Chronic obesity was defined as being obese at ≥ 3 of the 6 assessments between ages 15 and 38 years. Persistent heavy smoking was defined as smoking 20 or more cigarettes/day at ≥ 3 of the 6 assessments between ages 15 and 38 years. Cessation failure was defined for cohort members who smoked daily during their 30s as being unable to quit for at least 1 year through the time of the age 38-year assessment.

FIGURE 2—

Models of genetic contribution to social gradients in health. Note. The top row of the figure shows schematic graphs of the 3 models of gene–environment interplay. Rows 2–4 show how each of these models plays out within high, average, and low environmental risk strata. Under model 1 (gene plus environment [G+E]), genetic risk effects and genetic risk distributions are the same across strata of environmental risk; the social gradient in health arises purely from differences in environmental risk. Under model 2 (gene-environment correlation [rGE]), genetic risk effects are the same across strata of environmental risk. However, the population stratum exposed to the highest environmental risk also carries the highest genetic risk and the population stratum exposed to the lowest environmental risk carries the lowest genetic risk. Under model 3 (gene-environment interaction [G×E]) genetic risk effects are stronger when environmental risk is high and weaker when environmental risk is low. The distribution of genetic risk is the same across strata of environmental risk.

FIGURE 3—

Genetic risk by social class in the Dunedin Cohort for (a) obesity and (b) smoking: Dunedin Multidisciplinary Health and Development Study, 1972–2013. Note. Graphs depict the distribution of social class (bars) and average genetic risk scores for obesity and smoking (dots) and associated 95% confidence intervals. Social class was defined from parents’ occupational attainment. Genetic risk scores were standardized to have mean of zero and standard deviation of one. The dashed gray line shows average genetic risk in the population. Under the gene-environment correlation (rGE) model of genetic contribution to social gradients in health, genetic risk should be higher for children of lower social class (dots would trend downwards from left to right). The data in this figure show that Dunedin cohort members’ genetic risks for obesity and smoking were not associated with their parents’ occupational attainment, i.e. there was no evidence of gene-environment correlation (Pearson correlations r < 0.01 for both).

FIGURE 4—

Associations between genetic risk and lifetime cigarette consumption in cohort members with a history of childhood maltreatment and no history of childhood maltreatment: Dunedin Multidisciplinary Health and Development Study, 1972–2013. The scatter plots and regression lines show the association between genetic risk and lifetime cigarette consumption in pack-years by age 38 years for cohort members with a history of child maltreatment (black line, diamond plot) and for cohort members with no childhood maltreatment history (gray line, circle plot). These plots show that the association between genetic risk and smoking is stronger in cohort members who were maltreated as children (i.e., there is gene-environment interaction [G×E]). The kernel density plots in the background of the graph show the distribution of genetic risk in the 2 groups (dark gray for the maltreated cohort members, light gray for the non-maltreated cohort members). These plots show that the distribution of genetic risk is similar regardless of maltreatment history (i.e., there is no rGE).