TY - UNPB
T1 - Building machine learning prediction models for wellbeing using predictors from the exposome and genome in a population cohort
AU - Pelt, Dirk
AU - Habets, Philippe
AU - Vinkers, Christiaan H.
AU - Ligthart, Lannie
AU - van Beijsterveldt, CEM
AU - Pool, René
AU - Bartels, Meike
PY - 2023/10/13
Y1 - 2023/10/13
N2 - Using longitudinal data of a large population cohort (Netherlands Twin Register; collected 1991-2022), we aim to build machine learning prediction models for adult wellbeing from the exposome and genome, and identify the most predictive factors (Ns between 702 and 5874). The specific exposome was captured by parent- and self-reports of psychosocial factors from childhood to adulthood, the genome by polygenic scores, and the general exposome by linkage of participants’ postal codes to objective exposures based on registry data. The specific exposome was found to be highly predictive of wellbeing in an independent test set (R2 = .701), but not the genome (R2 = .008) and general exposome (R2 = -.006) independently, nor incrementally. Risk/protective factors such as optimism, personality, social support, and previous wellbeing levels were most predictive. Our findings highlight the importance of monitoring individuals over time and current limitations associated with prediction based on the genome and general exposome.
AB - Using longitudinal data of a large population cohort (Netherlands Twin Register; collected 1991-2022), we aim to build machine learning prediction models for adult wellbeing from the exposome and genome, and identify the most predictive factors (Ns between 702 and 5874). The specific exposome was captured by parent- and self-reports of psychosocial factors from childhood to adulthood, the genome by polygenic scores, and the general exposome by linkage of participants’ postal codes to objective exposures based on registry data. The specific exposome was found to be highly predictive of wellbeing in an independent test set (R2 = .701), but not the genome (R2 = .008) and general exposome (R2 = -.006) independently, nor incrementally. Risk/protective factors such as optimism, personality, social support, and previous wellbeing levels were most predictive. Our findings highlight the importance of monitoring individuals over time and current limitations associated with prediction based on the genome and general exposome.
U2 - 10.31234/osf.io/fsq3j
DO - 10.31234/osf.io/fsq3j
M3 - Preprint
BT - Building machine learning prediction models for wellbeing using predictors from the exposome and genome in a population cohort
ER -