Genetic influences on cognitive executive functions and related phenotypes
Principal Investigator: Dr Naomi Friedman
Approved Research ID: 24795
Approval date: January 15th 2017
We are interested in genetic associations between cognitive executive functions and behavior. Executive function deficits have been implicated in almost every form of psychopathology, and some of our research suggests these deficits relate to variance common across psychiatric symptoms. However, the extent to which these relations are genetic is not well established. The aim of this study is to evaluate the genetic influences on behavioral and neural measures related to executive functions and the extent to which these are shared with those for general cognitive ability, general and specific psychopathology, risky and sexual behaviors, and behavior problems more generally. Executive functions have been implicated in common psychiatric disorders, general health (diet, exercise, and sleep), and potentially harmful behaviors related to externalizing disorders (substance use, aggression, sexual behaviors, and risk-taking). Indeed, they have been proposed as endophenotypes for many disorders. However, the extent to which these links are genetic is not clear, nor are the neural substrates of individual differences in these abilities and behaviors. The UKbiobank data will allow new analyses of the nature of these relations, which can lead to a greater understanding of these cognitive abilities and related health outcomes. We will create composites of general intelligence, executive functions, and self-report psychosocial and health behaviors. We will use fMRI resting state correlation matrices to create a measure of task-positive to task-negative network connectivity and graph theoretic measures. We will then use genetic data to investigate whether certain genetic variants predict these measures, how well we can predict these measures by considering all genetic data simultaneously, and how these measures genetically correlate with each other. Our methods require large samples; we would like access to all whole genome genotyped data.