Toward Understanding Genetic Risk of Burnout through Genome-wide Associations of Cynicism and Cynical Distrust

Cynicism is an attitude that people are motivated primarily by self-interest, and often manifests alongside emotions like contempt, anger, and hostility. Cynicism can impact all aspects of an individuals life: in the workplace it is a critical component of burnout, while at home it often leads to marital dissatisfaction and increased family conflicts. Using data from 2,317 participants in the CARDIA Cohort study, we performed a genome-wide association study (GWAS) to identify genetic variants related to cynicism and the cynical distrust sub-trait. We performed two GWAS tests for each trait, one without controlling for population stratification and a second where we considered possible ancestry-related stratification using principal component analysis within plink 1.9. We also tested multiple genetic inheritance models. Finally, we performed pathway analysis using the STRINGdb web resource to identify meaningful relationships. From our various approaches, we identified 12 variants associated with high cynicism in an additive model and 258 in a genotypic model. We also identified 24 and 275 variants associated with cynical distrust using the respective models. Our network analysis revealed clear pathways for how cynicism can lead to health outcomes like cardiovascular disease, adrenal disorders, neurological disorders, and even addiction. Specifically, genes involved in 30 distinct KEGG pathways were related to cynicism and clear connections can be made between cynicism and cardiomyopathies, Cushing syndrome, Alzheimers disease, and amphetamine addiction. Each of the outcomes are supported by gene association with at least 3 intermediate pathways. Our results provide the first objective evidence supporting the observational literature connections to cardiovascular disease, Alzheimers disease, and addiction. To the best of our knowledge, this is the first evidence presenting a relationship between cynicism and Cushings syndrome, demonstrating the power of population and statistical genetics.