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Activity Number: 422 - Statistical Learning for Functional Data
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #330705 Presentation
Title: Quantifying Genetic Influences on Physical Activity Among Twins Based on Minute-Level Accelerometry Data Among Twins
Author(s): Haochang Shou* and Joanne Carpenter and Kathleen Merikangas and Ian Hickie
Companies: University of Pennsylvania and University of Sydney and National Institute of Mental Health and University of Sydney
Keywords: twin studies; accelerometry; functional data; heritability

Twin studies provide unique opportunities to estimate heritability of certain quantitative traits. The conventional ACE models via structural equation modeling have been used to segment and quantify the genetic and environmental influences in the sample. The emergence of complex measures generated from novel technologies such as wearable sensor devices has posed challenges on directly applying such models to time-dependent measures. Motivated by the physical activity data observed from Brisbane adolescent study, we extended the traditional ACE model for a single univariate trait to functional outcomes. In particular, the method simultaneously: 1) handle various levels of correlation in the data; 2) identify interpretable traits via dimensionality reduction based on principal components; and 3) estimate relative variances that are attributed by additive genetic, shared environmental and unique environmental effects. Within-family similarities of those complex measures could also be effectively quantified.The methods have been applied to identify heritable features and among physical activity measures and quantify the relative variability between genetics and environmental factors.

Authors who are presenting talks have a * after their name.

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