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Geijsen, A.

Publications and source records attributed to Geijsen, A..

2 recordsLinked to original sources

The multidimensional structure of wellbeing: genetic evidence from a multivariate twin study including the Mental Health Continuum

Wellbeing is commonly defined as the combination of feeling good and functioning well and typically conceptualized as two related but distinct components. Hedonic wellbeing emphasizes pleasure, happiness, and life satisfaction, while eudaimonic wellbeing focuses on meaning, personal growth, flourishing, and the realization of ones potential. The Mental Health Continuum-Short Form was developed as a comprehensive measure of wellbeing and includes three subscales assessing emotional, social, and psychological wellbeing. Although the Mental Health Continuum total score is often interpreted as an indicator of overall wellbeing, the underlying genetic structure of its three subscales and its genetic overlap with other commonly used wellbeing measures remains unclear. Using data from 5,212 individuals from the Netherlands Twin Register (72% female, mean age 36.4), we fitted multivariate twin models to examine the genetic architecture of the Mental Health Continuum and its associations with other wellbeing measures (quality of life, life satisfaction, subjective happiness, and flourishing). Results indicate that, at the genetic level, the Mental Health Continuum is best explained by its three distinct subscales rather than by a latent factor. When considering the Mental Health Continuum together with the other wellbeing measures, we found moderate to high genetic correlations (r = 0.52 - 0.83), indicating substantial overlap in the genetics underlying the wellbeing constructs. However, we did not find evidence for a single common genetic factor underlying all constructs. These findings highlight the multidimensional structure of wellbeing, but the moderate to high genetic correlations across measures suggest that it is important to align the level of measurement (phenotypic vs genetic) with the research question.

genetics↗

The Hidden Biology of Wellbeing: A Multi-Omics Analysis Across Genomic, Epigenomic, and Transcriptomic Layers.

Biological systems are composed of multiple molecular layers, including the genome, epigenome, transcriptome, and metabolome. These layers are interconnected and interact, but are often studied independently. Multi-omics approaches aim to integrate these layers to better understand shared biological processes and their roles in complex traits, such as wellbeing. This project used data from 2,320 participants from the Netherlands Twin Register (NTR), which includes genome, epigenome, and transcriptome data from the same individuals, providing a rare opportunity to study interactions between omics layers. Multi-Omics Factor Analysis (MOFA) was applied, an unsupervised dimensionality reduction method, to identify latent factors that capture shared variation across omics layers. These factors may reflect underlying biological mechanisms not directly observed in individual omics layers. The phenotypic focus of this study is wellbeing, a multifactorial trait involving emotional, psychological, and social dimensions. Following MOFA modeling, one latent factor showed to be statistically significantly associated with wellbeing. After studying the latent factor, it mainly seemed to capture epigenetic variation. The association between the latent factor and wellbeing did not remain significant after correcting for relevant covariates. The association was mainly driven by age, a well-known confounder of epigenetic signal. By adopting a multi-omics framework, this study managed to move beyond traditional single-omics, phenotype-driven approaches and provided a more integrated approach of how biological layers may jointly influence (or not) complex traits like wellbeing.

molecular biology↗