Genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders identifies five underlying genomic factors that explain the majority of the genetic variance of the individual disorders...

Kassandra Pomper studied Psychology at Vrije Universiteit Amsterdam. We asked her a few questions about her experience of the programme.

Meta-analyses report small to moderate effect sizes or inconsistent associations (usually around r = -0.10) between wellbeing (WB) and social media use (SMU) and between anxious-depressive symptoms (ADS) and SMU (also around r = 0.10). This study employs the classical twin design, utilizing data from 6492 individuals from the Netherlands Twin Register, including 3369 MZ twins (893 complete twin pairs, 1583 incomplete twin pairs) and 3123 DZ twins (445 complete, 2233 incomplete) to provide insights into the sources of overlap between WB/ADS and SMU. Both hedonic and eudaimonic WB scales were used. SMU was measured by (1) the time spent on different social media platforms (SMUt), (2) the frequency of posting on social media (SMUf), and (3) the number of social media accounts individuals have (SMUn). Our results confirmed the low phenotypic correlations between WB and SMU (between r = -0.09 and 0.04) as well as between ADS and SMU (between r = 0.07 and 0.10). For SMU, heritability estimates between 32 and 72% were obtained. The small but significant phenotypic correlations between WB/ADS and the SMU phenotypes were mainly determined by genetic factors (in the range of 80-90%). For WB and SMU, genetic correlations were between -0.10 and -0.0, and for ADS and SMU genetic correlations were between 0.10 and 0.23. Genetic correlations implied limited but statistically significant sets of genes that affect WB/ADS and SMU levels. Overall, the results indicate that there is evidence that the small associations between WB/ADS and SMU are partly driven by overlapping genetic influences. We encourage researchers and experts to consider more personalized approaches when considering the association between WB and SMU, as well as understanding the reasons for individuals’ observed SMU levels.