Research Project 2 - Genomic Approaches to Pollutome Effects on Risk of Major Depression in Hispanic Pedigrees

SUMMARY Major depressive disorder (MDD) is characterized by an extended episode of a persistent feeling of sadness or a lack of interest in outside stimuli. It is among the most common mental illnesses, affecting 16.2% of individuals in the US during their lifetime. MDD is a heterogeneous disorder with a variable clinical course, an inconsistent

response to treatment, and little established etiology. Arguably, our lack of understanding of the causes of the disorder hinders improvements in prevention, diagnosis, and treatment. Multiple risk factors predispose MDD, including demographic characteristics (e.g., sex, age, and ethnicity), behavior and lifestyle-related modifications (e.g., addiction, socioeconomic status, immigration status, stressful

life events), and both endogenous (e.g., infectious agents) and exogenous environmental factors (e.g., exposure to pollutants/contaminants/toxins). In addition, MDD risk is substantially heritable. However, our ability to identify novel environmental risk factors has been limited by a lack of sufficiently broad environmental measures.

Recently, evidence has been accumulating that exposure to pollutants influences the risk of MDD, although most studies have employed indirect exposure estimates. Here we propose to measure person-specific levels of organic and inorganic pollutants to search for environmental determinants of recurrent MDD (rMDD) risk in large

multigenerational pedigrees from the Mexican American Family Study (MAFS). A wealth of phenotypic and genetic information exists on the members of the randomly ascertained families in this cohort. Specifically, we previously documented high rates of depression in these families, estimated the heritability of rMDD (h2=0.46),

and localized genetic loci using linkage and whole genome sequencing (WGS) approaches. Recently, we developed a novel family-based approach to control for genetic factors and thereby increase the power to detect causal environmental signals influencing disease risk. This analytic approach makes it possible to optimally

detect novel environmentally driven determinants of rMDD risk. Given the pedigree-based design and preexisting phenotypic and WGS data, the MAFS cohort provides a powerful efficient resource for studying environmental components of rMDD risk and will provide important new insights into the etiology/mechanisms of MDD risk. Our

specific aims are: 1) to obtain individual-level direct measures of the pollutome including a set of 72 persistent organic pollutants and 28 metals in banked plasma samples from two time points and indirect spatially-imputed measures of air pollutant exposure; 2) to detect the influence of pollutants on rMDD risk using a novel statistical

approach to control for the effect of genetic factors to maximize environmental pollutant signals; 3) to detect genotype×pollutome interactions in rMDD risk; and 4) to replicate results in an independent set of 750 Mexican American individuals from the Rio Grande Valley Family Study using similar protocols to that of the MAFS.

Overall, our project proposes to use genomic tools in a novel way to enhance the identification of environmental risk factors and to foster the study of human genotype×environment interaction. The study also will provide an exciting resource for training students in the value of genomics for environmental epidemiology.