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Bullying research has shown repeatedly that victims of bullying have an increased risk for later internalizing problems and bullies have an increased risk for later externalizing problems. Bullying involvement is often, either explicitly or implicitly, presented as part of a causal mechanism for maladjustment. However, genetic vulnerability may confound the reported associations. This study examined to what extent genetic vulnerability can account for the reported associations between bullying involvement (age 11-14) and later internalizing and externalizing problems (age 16), using data from the TRacking Adolescents' Individual Lives Survey (n = 1604). Because polygenic scores capture only a fraction of the total genetic effect, they were extrapolated to the size of single-nucleotide polymorphism and twin heritability estimates to examine genetic confounding while controlling for (hypothetical) polygenic scores that fully capture the genetic effect. Genetic vulnerability for internalizing and externalizing problems confounded, respectively, the association between bullying victimization and later internalizing problems, and the association between bullying perpetration and later externalizing problems. As such, this study showcases a method that can be broadly used to assess the magnitude of genetic confounding. Caution is, however, warranted in interpreting particularly the less straightforward extrapolations of polygenic scores to the size of twin heritability estimates.
Many adolescents start using tobacco, alcohol, and cannabis. Genetic vulnerability, parent characteristics in young adolescence, and interaction (GxE) and correlation (rGE) between these factors could contribute to the development of substance use. Using prospective data from the TRacking Adolescent Individuals’ Lives Survey (TRAILS; N = 1,645), we model latent parent characteristics in young adolescence to predict young adult substance use. Polygenic scores (PGS) are created based on genome-wide association studies (GWAS) for smoking, alcohol use, and cannabis use. Using structural equation modeling we model the direct, GxE, and rGE effects of parent factors and PGS on young adult smoking, alcohol use, and cannabis initiation. The PGS, parental involvement, parental substance use, and parent–child relationship quality predicted smoking. There was GxE such that the PGS amplified the effect of parental substance use on smoking. There was rGE between all parent factors and the smoking PGS. Alcohol use was not predicted by genetic or parent factors, nor by interplay. Cannabis initiation was predicted by the PGS and parental substance use, but there was no GxE or rGE. Genetic risk and parent factors are important predictors of substance use and show GxE and rGE in smoking. These findings can act as a starting point for identifying people at risk.
Twin studies have found that ~50% of variance in electrocardiogram (ECG) traits can be explained by genetic factors. However, genetic variants identified through genome-wide association studies explain less than 10% of the total trait variability. Some have argued that the equal environment assumption for the classical twin model might be invalid, resulting in inflated narrow-sense heritability (h2) estimates, thus explaining part of the ‘missing h2’. Genomic relatedness restricted maximum likelihood (GREML) estimation overcomes this issue. This method uses both family data and genome-wide coverage of common SNPs to determine the degree of relatedness between individuals to estimate both h2 explained by common SNPs and total h2. The aim of the current study is to characterize more reliably than previously possible ECG trait h2 using GREML estimation, and to compare these outcomes to those of the classical twin model. We analyzed ECG traits (heart rate, PR interval, QRS duration, RV5+SV1, QTc interval, Sokolow-Lyon product, and Cornell product) in up to 3,133 twins from the TwinsUK cohort and derived h2 estimates by both methods. GREML yielded h2 estimates between 47% and 68%. Classical twin modeling provided similar h2 estimates, except for the Cornell product, for which the best fit included no genetic factors. We found no evidence that the classical twin model leads to inflated h2 estimates. Therefore, our study confirms the validity of the equal environment assumption for monozygotic and dizygotic twins and supports the robust basis for future studies exploring genetic variants responsible for the variance of ECG traits.
Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.
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