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Background: The non-cognitive skills self-control and grit have often been cited as predictors of school performance, but little research has investigated whether this relationship is causal. We investigated the causal nature of this association in a classical twin design, with mono- and dizygotic twin pairs. Specifically, we evaluated the direct impact of self-control and grit on school performance, while controlling for genetic or environmental influences common to all three traits (i.e., confounding). Methods: Teachers of 4,891 Dutch 12-year-old twin pairs (of which 3,837 were complete pairs) completed a survey about school performance (school grades), self-control (ASEBA self-control scale), and the perseverance aspect of grit. We regressed school performance on self-control and grit within the twin model to establish the phenotypic, putatively causal, regression relationship. We modelled genetic or environmental confounding (influences common to the three phenotypes) to determine their influence. In all analyses, we corrected for sex, rater effects of the teacher, and parental socioeconomic status. Results: Self-control and grit explained 28.4% of the school-performance variance in the phenotypic regression analysis (assuming no confounding). However, allowing for genetic confounding (due to genetic pleiotropy) revealed that the association was largely attributable to genetic influences that the three traits share. In the presence of genetic pleiotropy, the phenotypic regression of school performance on self-control and grit accounted for only 4.4%. Conclusions: The association between self-control and grit as predictors of school performance is attributable to both genetic pleiotropy, and to a lesser extent, direct effects of self-control and grit on school performance, which are putatively causal.

Background: By combining the classical twin design with regression analysis, we investigated the role of two non-cognitive factors, self-control and grit, in the prediction of school performance. We did so at the phenotypic, genetic, and environmental level. Methods: Teachers filled out a survey on the twins’ school performance (school grades for reading, literacy, and math), self-control (ASEBA self-control scale), and grit (the perseverance aspect) for 4,891 Dutch 12-years-old twin pairs (3,837 pairs with data for both and 1,054 pairs with data for one of the twins). We employed regression analyses to first assess the contributions of self-control and grit to school performance at the phenotypic level, and next at the genetic and environmental level, while correcting for rater (teacher) effects, parental SES, and sex. Results: Higher SES was associated with better school performance, self-control, and grit. On average, girls had more self-control and grit than boys. Corrected for sex, SES, and teacher rater effects, genetic factors accounted for 74%, 69%, and 58% of the phenotypic variance of school performance, self-control, and grit, respectively. Phenotypically, self-control and grit explained 28.3% of the variance in school performance. We found that this phenotypic result largely reflected genetic influences. Conclusions: Children who have better self-control and are grittier tend to do better in school. Individual differences in these three traits are not correlated because of shared environmental influences, but mainly because of shared genetic factors.

In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes, and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.

There is a negative association between intelligence and psychopathology, implying that individuals with intellectual disability are at greater risk for psychopathology. We analyzed data on intelligence and psychopathology in seven-year-old twin pairs (npairs ≥ 616) to determine the degree to which genetic and environmental factors common to intelligence and psychopathology explain the association, and to examine whether genetic and environmental effects on psychopathology are moderated by intelligence. We found that intelligence correlated negatively with internalizing, externalizing, and neurodevelopmental psychopathology. The associations were mainly due to common genetic factors. Intelligence moderated genetic and environmental effects on internalizing psychopathology (anxiety and negative affect), but not those on externalizing and neurodevelopmental psychopathology. The heritability of anxiety and negative affect was greatest in individuals with below-average intelligence. We discuss mechanisms through which this effect could arise, and we end with some recommendations for future research.

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.

Background: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50–70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. Methods: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. Findings: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07–1·15, p=1·84 × 10 −9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86–0·93, p=6·46 × 10 −9). Cannabis use disorder and cannabis use were genetically correlated (r g 0·50, p=1·50 × 10 −21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. Funding: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Worldwide, the majority of people prefer using the right hand for most motor tasks, including writing. Because of the link between handedness and language hemispheric dominance, handedness has been studied for association with language-related disorders. No clear pattern has emerged from these studies, and inconsistencies have been attributed to small sample sizes, publication bias, and heterogeneous criteria for the definition of handedness and disorders.Here, we assessed the frequency of non-right handedness (NRH) in 10 distinct cohorts not analysed before in this context. We identified N = 2,499 cases with reading and/or language impairment and N = 4,428 unique controls on the basis of a priori defined criteria. Overall, NRH was more frequent and more variable in the cases (8-24%) than in the controls (8-16%). Meta-analysis in the eight cohorts that met the inclusion criteria showed an increase of NRH in individuals with language/reading impairment compared to controls (OR = 1.21, CI = 1.06 - 1.37, p = 0.009). No moderator effects were detected for type of cohort (epidemiological versus clinical) and type of impairment (language versus reading). Our results support a genuine but modest association between NRH and reading and language impairments, suggesting shared biological pathways underlying brain lateralization, handedness, and cognitive functions.

Handedness has low heritability and epigenetic mechanisms have been proposed as an etiological mechanism. To examine this hypothesis, we performed an epigenome-wide association study (EWAS) of left-handedness. In a meta-analysis of 3,914 adults of whole-blood DNA methylation, we observed that CpG sites located in proximity of handedness-associated genetic variants were more strongly associated with left-handedness than other CpG sites (P = 0.04), but did not identify any differentially methylated positions. We identified differentially methylated regions at 20q11.23 (P = 0.00004) and 2p25.1 (P = 0.03), which were less methylated in left-handed adults. In longitudinal analyses of DNA methylation in peripheral blood and buccal cells from children (N = 1,737), we observed moderately stable associations across age (correlation range [0.355–0.578]) but inconsistent across tissues (correlation range [-0.384-0.318]). We conclude that DNA methylation in peripheral tissues captures little of the variance in handedness. Future investigations should consider other more targeted sources of tissue, such as the brain.

In deze kroniek beargumenteren wij dat sociale en genetische verklaringen voor gedrag niet los van elkaar gezien kunnen worden, en dat onderzoekdesigns die gebruik maken van inzichten uit de genetica een waardevolle toevoeging kunnen zijn aan de ‘toolbox’ die criminologen voorhanden hebben om onderzoek te doen naar causale verbanden en intergenerationele continuïteit. Vanuit dit perspectief behandelen we vier onderzoekbenaderingen: familie- en tweelingdesigns, genoomwijde associatiestudies, polygenetische scores en Mendelian Randomization. Tot slot bespreken we een aantal praktische overwegingen bij het toepassen van genetische onderzoekdesigns in de criminologie.

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown.We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48 % male) derived from 15 distinct MRI scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers – the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Figure S2. QQ plot for genetic association analysis of cotinine levels and 46,780 SNPs in 40 genes. (PDF 17 kb)

Figure S3. Mediation analysis to assess whether DNA methylation is a causal mediator to the observed association between genetic variants and cotinine levels. (PDF 588 kb)

Additional file 3. DHS enrichment for methylation sites with large MZ twin correlation, adjusted for cellular composition.

Additional file 9. Chromatin state enrichment for methylation sites with the strongest mQTLs.

Additional file 7. Histone H3 mark enrichment for methylation sites with large MZ twin correlation, adjusted for cellular composition.

Additional file 1. Table S1: MZ twin correlations for DNA methylation level at all autosomal methylation sites, without adjustment for cellular composition. Figure S1: Scatterplots of methylation β-values of matched samples on EPIC and HM450. Figure S11: Quality control plot of bisulfite conversion. Figure S12: Quality control plot of overall sample quality based on sample-dependent control probes (Non-Polymorphic quality control probes). Figure S13: Quality control plot of the median Methylated versus Unmethylated signal intensity. Figure S14: Quality control plot based on sample-independent hybridization control probes. Figure S15: Quality control plot showing the proportion of probes with a detection p-value < 0.01 within samples. Figure S16: Scree plots of PCs based on control probes. Figure S17: Heatmap of the correlations of technical and biological variables with PCs based on the genome-wide methylation data prior to normalization. Figure S18: Heatmap of the correlations of technical and biological variables with PCs based on the genome-wide methylation data after functional normalization. Figure S19: IBS mean-variance plot from omicsPrint of samples measured on EPIC. Figure S20: IBS mean-variance plot from omicsPrint of matched samples measured on EPIC and HM450.

Additional file 6. Histone H3 mark enrichment for methylation sites with large MZ twin correlation, unadjusted for cellular composition.

Additional file 8. DHS enrichment for methylation sites with the strongest mQTLs.

Additional file 4. Chromatin state enrichment for methylation sites with large MZ twin correlation, unadjusted for cellular composition.

Additional file 5. Chromatin state enrichment for methylation sites with large MZ twin correlation, adjusted for cellular composition.

Table S1. Inclusion and exclusion criteria. (DOCX 36Â kb)

Additional file 2. DHS enrichment for methylation sites with large MZ twin correlation, unadjusted for cellular composition.

Table S3. Questionnaires baseline. (DOCX 37Â kb)

Additional file 10. Histone H3 mark enrichment for methylation sites with the strongest mQTLs.

Sample size, mean, standard deviation and range for age by cohort in females from same- and opposite-sex dizygotic twin pairs. Table S2. Sample size, mean, standard deviation, and range for age by cohort in males from same- and opposite-sex dizygotic twin pairs. Table S3. Sample size, mean, and standard deviation for height (cm) and BMI (kg/m2) by cohort in females from same- and opposite-sex dizygotic twin pairs. Table S4. Sample size, mean, and standard deviation for height (cm) and BMI (kg/m2) by cohort in males from same- and opposite-sex dizygotic twin pairs. (DOCX 45 kb)

Variability at three top-ranked pan-cancer DMPs in 9 cancer discordant and 480 healthy MZ twin-pairs. Histogram with density overlay of absolute differences of 480 healthy MZ twin-pairs with median (blue) and density and median of differences between 9 cancer discordant MZ twin-pairs (red). At [A] cg0244695 near SASH1, [B] cg26079695 in COL11A2, and [C] cg27094856 in AXL. (PDF 143Â kb)

Replication of four top-ranked pan-cancer DMPs in 9 cancer discordant MZ twin-pairs. Direction of association at [A] cg0244695 near SASH1, [B] cg26079695 in COL11A2, [C] cg27094856 in AXL, and [D] cg21046959 in LINC00340. Results are plotted using normalised unadjusted beta values of cancer-affected individuals (left) and healthy individuals (right). The lines connect co-twins in twin-pairs and indicate a consistent direction of effect. (PDF 88Â kb)

DNA methylation was associated with gene expression. (PDF 21974Â kb)

The EWAS effect size estimates were not sensitive to the exclusion of non-fasted samples, the exclusion of samples with imputed cell counts, or the addition of current smoking behavior, lipid-lowering medication, or BMI as covariates. (PDF 273Â kb)

Simulation showing the effect of confounders on network reconstruction. (PDF 117 kb)

Description of external datasets used. (PDF 79 kb)

Estimates and 95Â % confidence intervals for the EWAS-identified CpGs for each cohort contributing to the meta-analysis for TG, LDL-C, and HDL-C. (PDF 297Â kb)

Replication of four top-ranked pan-cancer DMPs in 9 cancer discordant MZ twin-pairs. Direction of association at [A] cg0244695 near SASH1, [B] cg26079695 in COL11A2, [C] cg27094856 in AXL, and [D] cg21046959 in LINC00340. Results are plotted using normalised unadjusted beta values of cancer-affected individuals (left) and healthy individuals (right). The lines connect co-twins in twin-pairs and indicate a consistent direction of effect. (PDF 88Â kb)

BIOS Consortium (Biobank-based Integrative Omics Study). (PDF 68 kb)

Variability at three top-ranked pan-cancer DMPs in 9 cancer discordant and 480 healthy MZ twin-pairs. Histogram with density overlay of absolute differences of 480 healthy MZ twin-pairs with median (blue) and density and median of differences between 9 cancer discordant MZ twin-pairs (red). At [A] cg0244695 near SASH1, [B] cg26079695 in COL11A2, and [C] cg27094856 in AXL. (PDF 143Â kb)

Quantile–quantile plots depicting the associations between TG, LDL-C, and HDL-C and genome-wide DNA methylation when compared with a uniform distribution expected by chance for a uncorrected meta-analysis z-values and b meta-analysis z-values corrected for genome-wide inflation using genomic control (λ = 1.0). (PDF 14800 kb)

Comparison between EWAS and MR estimates (with 95Â % confidence intervals). (PDF 109Â kb)

Pan-cancer top-ranked results in 41 discordant MZ twin-pairs. Direction of association at [A] cg26079695 in COL11A2, [B] cg27094856 in AXL, and [C] cg21046959 in LINC00340. Results are plotted using normalised unadjusted beta values of cancer-affected individuals (left) and healthy individuals (right). The lines connect co-twins in twin-pairs and indicate a consistent direction of effect. (PDF 78Â kb)

Differential methylation with respect to age and time of cancer diagnosis. [A] Time to diagnosis compared to age at blood sample collection, the line represent the least squares regression fit. [B] Unadjusted DNA methylation values at cg02444695 (near SASH1) in affected individuals (red) and healthy co-twins (blue), shown with respect to age at blood sample collection (years) with smooth (LOESS) lines fitted for both groups. [Câ F] Unadjusted DNA methylation values in affected individuals (red) and healthy co-twins (blue), shown with respect to time of diagnosis (years) with smooth (LOESS) lines fitted for both groups with blood samples collected 5 to 11Â years before cancer diagnosis. The orange and black vertical lines represent the time of diagnosis and time window of the main study respectively. At [C] cg0244695 near SASH1, [D] cg26079695 in COL11A2, [E] cg27094856 in AXL, and [F] cg21046959 in LINC00340. (PDF 185Â kb)

Differential methylation with respect to age and time of cancer diagnosis. [A] Time to diagnosis compared to age at blood sample collection, the line represent the least squares regression fit. [B] Unadjusted DNA methylation values at cg02444695 (near SASH1) in affected individuals (red) and healthy co-twins (blue), shown with respect to age at blood sample collection (years) with smooth (LOESS) lines fitted for both groups. [Câ F] Unadjusted DNA methylation values in affected individuals (red) and healthy co-twins (blue), shown with respect to time of diagnosis (years) with smooth (LOESS) lines fitted for both groups with blood samples collected 5 to 11Â years before cancer diagnosis. The orange and black vertical lines represent the time of diagnosis and time window of the main study respectively. At [C] cg0244695 near SASH1, [D] cg26079695 in COL11A2, [E] cg27094856 in AXL, and [F] cg21046959 in LINC00340. (PDF 185Â kb)

This study estimated the heritability of 24-h heart rate variability (HRV) measures, while considering ceiling effects on HRV at low heart rates during the night. HRV was indexed by the standard deviation of all valid interbeat intervals (SDNN), the root mean square of differences between valid, successive interbeat intervals (RMSSD), and peak-valley respiratory sinus arrhythmia (pvRSA). Sleep and waking levels of cardiac vagal control were assessed in 1,003 twins and 285 of their non-twin siblings. Comparable heritability estimates were found for SDNN (46%-53%), RMSSD (49%-54%), and pvRSA (48%-57%) during the day and night. A nighttime ceiling effect was revealed in 10.7% of participants by a quadratic relationship between mean pvRSA and the interbeat interval. Excluding these participants did not change the heritability estimates. The genetic factors influencing ambulatory pvRSA, RMSSD, and SDNN largely overlap. These results suggest that gene-finding studies may pool the different cardiac vagal indices and that exclusion of participants with low heart rates is not required.