Genetic studies have now begun to quantitatively assay endophenotypes related to human health and behavior, e.g., sleep and activity levels, by leveraging the vast amount of longitudinal information generated from internet-connected devices, e.g., smartphones and smartwatches. Combining such data with genotype and sequence data will not only enable genetic discovery research (since such endophenotypes might have a strong genetic component) but also help prioritize relevant environmental exposures and behaviors that can module disease risk. This is particularly relevant for psychiatric research, which currently lacks large cohorts phenotyped for quantitative traits. In collaboration with Drs. Jonathan Flint, Nelson Freimer, and Michelle Craske, and as part of the UCLA Depression Grand Challenge, we are developing statistical methods for integrating large, longitudinally collected, digital behavioral health tracking datasets from ethnically diverse cohorts in genetics research of neuropsychiatric disorders. See our latest preprint here.