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The Barker Hypothesis posits that adverse intrauterine environments result in fetal growth restriction and increased risk of cardiometabolic disease through developmental compensations. Here we introduce a new statistical model using the genomic SEM software that is capable of simultaneously partitioning the genetic covariation between birthweight and cardiometabolic traits into maternally mediated and offspring mediated contributions. We model the covariance between birthweight and later life outcomes, such as blood pressure, non-fasting glucose, blood lipids and body mass index in the Norwegian HUNT study, consisting of 15,261 mother-eldest offspring pairs with genetic and phenotypic data. Application of this model showed some evidence for maternally mediated effects of systolic blood pressure on offspring birthweight, and pleiotropy between birthweight and non-fasting glucose mediated through the offspring genome. This underscores the importance of genetic links between birthweight and cardiometabolic phenotypes and offer alternative explanations to environmentally based hypotheses for the phenotypic correlation between these variables.

Both common and rare genetic variants (minor allele frequency >1% and

BackgroundHaving twin and non-twin siblings might influence autistic traits both prenatally and postnatally. The twin testosterone transfer hypothesis suggests that girls with a twin brother are exposed to higher levels of prenatal testosterone than girls with a twin sister, and that increased testosterone exposure masculinizes neural development and increases autistic traits. Postnatally, siblings may provide example behaviour, which could reduce autistic traits.MethodsWe studied pre- and postnatal influences of twin and non-twin siblings on mother and teacher-reported autistic traits in 7714 dizygotic twins. We examined the effect of sex of the proband child and of the siblings. We fitted regression models (for boys and girls separately) with sex of co-twin and having older and/or younger siblings of each sex as predictors.ResultsGirls' mother-reported autistic traits were slightly lower for those with a twin brother than those with a twin sister (β = −.08, p = .001, Cohen's d = −.13). This difference was not replicated in teacher-reported autistic traits (β = .01, p = .734). Boys' (mother and teacher-reported) autistic traits were not related to the sex of their co-twin (ps > 0.50). Teacher-reported autistic traits were slightly higher if girls had an older brother (β = .07, p = .013, Cohen's d = .12). Other than this small effect, we found no effect of non-twin siblings on autistic traits in either girls or boys (ps > .18).ConclusionsWe did not find increased autistic traits in girls with a twin brother compared to girls with a twin sister. This finding contributes to a body of literature that rejects the twin testosterone transfer hypothesis. In addition, we found little evidence for pre- and postnatal sibling influences. Our findings align with high heritability and absence of shared-environmental influences in ASD.

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.

BACKGROUND: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.METHODS: We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.RESULTS: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.CONCLUSIONS: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

Elevated C-reactive protein (CRP) concentrations in the blood are associated with acute and chronic infections and inflammation. Nevertheless, the functional role of increased CRP in multiple bacterial and viral infections as well as in chronic inflammatory diseases remains unclear. Here, we studied the relationship between CRP and gene expression levels in the blood in 491 individuals from the Estonian Biobank cohort, to elucidate the role of CRP in these inflammatory mechanisms. As a result, we identified a set of 1,614 genes associated with changes in CRP levels with a high proportion of interferon-stimulated genes. Further, we performed likelihood-based causality model selection and Mendelian randomization analysis to discover causal links between CRP and the expression of CRP-associated genes. Strikingly, our computational analysis and cell culture stimulation assays revealed increased CRP levels to drive the expression of complement regulatory protein CD59, suggesting CRP to have a critical role in protecting blood cells from the adverse effects of the immune defence system. Our results show the benefit of integrative analysis approaches in hypothesis-free uncovering of causal relationships between traits., Author summary Chronic inflammation is associated with chronic diseases, morbidity and mortality while lower base inflammation levels are thought to be predictive of healthy aging. Thus, to pursue a long and healthy lifespan, it is essential to understand the inflammatory regulatory mechanisms. To that end, we studied the functional role of C-reactive protein (CRP)–an inflammatory biomarker that is used to measure cardiovascular risk in clinical practice. There is evidence for a strong genetic component of elevated CRP levels but it is still unclear if it has a direct impact on the processes that lead to inflammatory diseases. In order to elucidate the function of CRP in the blood, we used statistical methods for causal inference to infer causal relationships between changes in CRP and gene expression levels. Our statistical analysis and cell culture experiments suggest that CRP drives the expression of complement regulatory protein CD59. Thus, CRP can have a functional role in protecting human blood cells from the adverse effects of the immune defence system.

partly overlapping individuals were genotyped, phenotyped and their data analyzed in genetic association studies, reflecting a huge communal effort by the substance use/addiction genetics community. These genome-wide association study (GWAS) efforts considered different stages of substance use: lifetime use (ever versus never use) was analyzed for cannabis and smoking, quantity of use (in users) was analyzed for coffee, alcohol, and smoking and age of initiation and cessation were analyzed for smoking. There are other GWA efforts and publications in the realm of addiction (see ref. 5), but here we limit ourselves to the largest meta-analyses per substance in order to maximize power. The GWA meta-analyses of substance-related traits identified many substance-specific genetic variants of moderate to small effect, which provided insight in the genetic etiology of substance use and its comorbidities. There are substantial phenotypic correlations among use of different substances, and both twin and polygenic risk prediction studies have shown that these phenotypic correlations are partly due to common genetic influences. 6, 7 Here we estimate genetic correlations (rg) between substance use-related variables based on the GWA summary statistics. These estimates of rg are based on all polygenic effects captured by single nucleotide polymorphisms. We used the recently developed linkage disequilibrium (LD) score regression method to estimate the proportion of covariance between traits that is due to single nucleotide polymorphisms, based on the expected relationship between LD and strength of association under a polygenic model. 8,9 The genetic correlation matrix revealed important information about common versus substance-specific genetic effects as well as specific patterns of cross-substance comorbidity (Figure 1). The substantial negative correlation between smoking cessation and smoking initiation reveals that the genes that predispose to initiation are negative predictors of success at cessation. Likewise, the genes that predispose individuals to smoke more cigarettes per day are negative predictors of successful cessation. Age at first cigarette is only associated with smoking initiation, not with cigarettes per day or smoking cessation. Interestingly, high genetic correlations are also observed across substance, between cannabis initiation and smoking initiation (rg=0.83, se=0.148), but also between quantity of nicotine consumption (cigarettes per day) and quantity of coffee consumed (cups per day) (rg=0.44, se=0.151), between coffee consumed and nicotine consumption (rg=0.38, se=0.16), and between alcohol consumption (alcohol per week) and cigarettes per day (rg=0.44, se=0.17). Most significant cross-substance correlations reflect genetic correlations within stage. However, both coffee per day and cigarettes per day are negatively associated with successful smoking cessation, indicating that frequent use, irrespective of substance, is genetically related to more problematic use of a different substance. The pattern of correlations observed implies a genetic model for substance use where both substance-specific and stagespecific genetic effects play a role. GWA meta-analyses of smoking, alcohol, cannabis and coffee use have shed light on the specific genetic effects for each substance. Here we show substance- and stage-specific GWAS results can be leveraged to elucidate the genetic architecture of substance use vulnerability in general. The next generation of large well-powered substance use GWA studies should systematically target all stages of use, for a broad spectrum of substances (e.g., cocaine and sugar rich foods) or addictive behavior (e.g., gambling, gaming and compulsive Internet use). Such an effort can aid in distinguishing between genes that are substance specific from genes that contribute to a specific stage of use, irrespective of substance or addictive behavior.

The methylome is subject to genetic and environmental effects. Their impact may depend on sex and age, resulting in sex- and age-related physiological variation and disease susceptibility. Here we estimate the total heritability of DNA methylation levels in whole blood and estimate the variance explained by common single nucleotide polymorphisms at 411,169 sites in 2,603 individuals from twin families, to establish a catalogue of between-individual variation in DNA methylation. Heritability estimates vary across the genome (mean=19%) and interaction analyses reveal thousands of sites with sex-specific heritability as well as sites where the environmental variance increases with age. Integration with previously published data illustrates the impact of genome and environment across the lifespan at methylation sites associated with metabolic traits, smoking and ageing. These findings demonstrate that our catalogue holds valuable information on locations in the genome where methylation variation between people may reflect disease-relevant environmental exposures or genetic variation., Differential impact of genetic and environmental influences on DNA methylation may result in sex- and age-related physiological variation and disease susceptibility. By analysing DNA methylome of 2,603 individuals from twin families, here, the authors establish a catalogue of between-individual variation in DNA methylation.

BackgroundGenetic research has made continuous progress in the past years, as a results of the rapidly growing sample sizes and development of novel analytical tools.AimTo present the latest developments in research into the genetic overlap and differences between psychiatric disorders.MethodDescription of findings from recent literature.ResultsFirst of all, psychiatric disorders differ in their heritability, and consequently impact of environmental factors. Second, some disorders show high concordance in direction of genetic effects, while other disorders have rather different effects. Using these genetic (dis)similarities, subgroups of psychiatric disorders can also be defined. ConclusionGenetic research is developing at rapid pace, and much more will be learned about the genetic overlap and differences between psychiatric disorders in the next years. The big promise -and challenge- will be to integrate genetic research with research of environmental factors, other biological measures and more detailed phenotypic information.