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Detecting epistatic drivers of human phenotypes remains a challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving single pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests from interactions by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that evaluate the stability of improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline using the phenotype of red-hair from the UK Biobank, where several genes are known to demonstrate epistatic interactions. epiTree recovers both previously reported and novel interactions, which represent forms of non-linearities not captured by logistic regression models. Additionally, epiTree suggests interactions between genes such as PKHD1 and XPOTP1, which are unlinked to MC1R, as novel candidate interactions associated with the red hair phenotype. Last but not least, we find that individual Boolean or tree-based epistasis models generally provide higher prediction accuracy than classical logistic regression.

Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities. Results: A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607, DLEU1 , P =1.8×10 −9 ), rs35991305 (chr12:94191968, CRADD , P =3.4×10 −8 ), and chr17:45013271:C:T ( GOSR2 , P =5.6×10 −8 ). Replication on an independent set of 8145 unrelated European ancestry participants showed consistent effect sizes in all 3 loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311 728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (odds ratio, 1.14; P =2.3×10 −6 ). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308 683 individuals), phenome-wide association of >10 000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birth weight along with other cardiovascular conditions. Conclusions: These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and non-coding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and non-coding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based datasets in quantifying the specific risk of individual variants for disease related phenotypes.

Among the major psychiatric disorders, anxious-depressive disorders stand out as one of the more prevalent and more frequently associated with hypothalamic-pituitary-adrenal (HPA) axis abnormalities. Methylation at the exon 1F of the glucocorticoid receptor gene NR3C1 has been associated with both early stress exposure and risk for developing a psychiatric disorder; however, other NR3C1 promoter regions have been underexplored. Exon 1D emerges as a suggestive new target in stress-related disorders epigenetically sensitive to early adversity. After assessment of 48 monozygotic twin pairs (n=96 subjects) informative for lifetime history of anxious-depressive disorders, they were classified as concordant, discordant or healthy in function of whether both, one or neither twin in each pair had a lifetime diagnosis of anxious-depressive disorders. DNA for epigenetic analysis was extracted from peripheral blood. Exon 1F and exon 1D CpG-specific methylation was analysed by means of pyrosequencing technology. Functional magnetic resonance imaging was available for 54 subjects (n=27 twin pairs). Exon 1D CpG-specific methylation within a glucocorticoid responsive element (GRE) was correlated with familial burden of anxious-depressive disorders (r=0.35, z=2.26, p=0.02). Right hippocampal connectivity was significantly associated with CpG-specific GRE methylation (β=-2.33, t=-2.85, p=0.01). Exon 1F was uniformly hypomethylated across all subgroups of the present sample. GRE hypermethylation at exon 1D of the NR3C1 gene in monozygotic twins concordant for anxious-depressive disorders suggests this region plays a role in increasing vulnerability to psychosocial stress, partly mediated by altered hippocampal connectivity.

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging. The work was supported by the European Commission’s 7th Framework Programme (#602450, IMAGEMEND), Research Council of Norway (213837, 223273, 204966/F20), the South-Eastern Norway Regional Health Authority (2013123, 2014097, 2015073, 2016083), The Norwegian Health Association's Dementia Research Program, and KG Jebsen Foundation. We acknowledge the contribution of patient data from the Norwegian registry for persons being evaluated for cognitive symptoms in specialized care (NorCog) by the Norwegian National Advisory Unit on Ageing and Health. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Although cerebellar involvement across a wide range of cognitive and neuropsychiatric phenotypes is increasingly being recognized, previous large-scale studies in schizophrenia (SZ) have primarily focused on supratentorial structures. Hence, the across-sample reproducibility, regional distribution, associations with cerebrocortical morphology and effect sizes of cerebellar relative to cerebral morphological differences in SZ are unknown. We addressed these questions in 983 patients with SZ spectrum disorders and 1349 healthy controls (HCs) from 14 international samples, using state-of-the-art image analysis pipelines optimized for both the cerebellum and the cerebrum. Results showed that total cerebellar grey matter volume was robustly reduced in SZ relative to HCs (Cohens's d= - 0.35), with the strongest effects in cerebellar regions showing functional connectivity with frontoparietal cortices (d=-0.40). Effect sizes for cerebellar volumes were similar to the most consistently reported cerebral structural changes in SZ (e.g., hippocampus volume and frontotemporal cortical thickness), and were highly consistent across samples. Within groups, we further observed positive correlations between cerebellar volume and cerebral cortical thickness in frontotemporal regions (i.e., overlapping with areas that also showed reductions in SZ). This cerebellocerebral structural covariance was strongest in SZ, suggesting common underlying disease processes jointly affecting the cerebellum and the cerebrum. Finally, cerebellar volume reduction in SZ was highly consistent across the included age span (16-66 years) and present already in the youngest patients, a finding that is more consistent with neurodevelopmental than neurodegenerative etiology. Taken together, these novel findings establish the cerebellum as a key node in the distributed brain networks underlying SZ.

A popular way to estimate the causal effect of a variable x on y from observational data is to use an instrumental variable (IV): a third variable z that affects y only through x. The more strongly z is associated with x, the more reliable the estimate is, but such strong IVs are difficult to find. Instead, practitioners combine more commonly available IV candidates---which are not necessarily strong, or even valid, IVs---into a single "summary" that is plugged into causal effect estimators in place of an IV. In genetic epidemiology, such approaches are known as allele scores. Allele scores require strong assumptions---independence and validity of all IV candidates---for the resulting estimate to be reliable. To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner. Theoretically, we characterize this robustness, its limits, and its impact on the resulting causal estimates. Empirically, Ivy can correctly identify the directionality of known relationships and is robust against false discovery (median effect size = 0.118).

SUMMARYMendelian Randomization (MR) is an important causal inference method primarily used in biomedical research. This work applies contemporary techniques in machine learning to improve the robustness and power of traditional MR tools. By denoising and combining candidate genetic variants through techniques from unsupervised probabilistic graphical models, an influential latent instrumental variable is constructed for causal effect estimation. We present results on identifying relationships between biomarkers and the occurrence of coronary artery disease using individual-level real-world data from UK-BioBank via the proposed method. The approach, termed Instrumental Variable sYnthesis (IVY) is proposed as a complement to current methods, and is able to improve results based on allele scoring, particularly at moderate sample sizes.

Congenital heart disease is the most common birth defect in newborns and the leading cause of death in infancy, affecting nearly 1% of live births. A locus in chromosome 4p16, adjacent to MSX1 and STX18, has been associated with atrial septal defects (ASD) in multiple European and Chinese cohorts. Here, genotyping data from the UK Biobank was used to test for associations between this locus and congenital heart disease in adult survivors of left ventricular outflow tract obstruction (n=164) and ASD (n=223), with a control sample of 332,788 individuals, and a meta-analysis of the new and existing ASD data was performed.The results show an association between the previously reported markers at 4p16 and risk for either ASD or left ventricular outflow tract obstruction, with effect sizes similar to the published data (OR between 1.27-1.45; all pp−4) for the association with ASD.The findings show that the genetic associations with ASD can be generalized to adult survivors of both ASD and left ventricular lesions. Although the 4p16 associations are statistically compelling, the mentioned alleles confer only a small risk for disease and their frequencies in this adult sample are the same as in children, likely limiting their clinical significance. Further epidemiological and functional studies may elicit factors triggering disease in interaction with the risk alleles.

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8–89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health. This is an Accepted Manuscript of an article published by Nature Publishing Group, available online: http://dx.doi.org/10.1038/s41380-020-0664-1

BACKGROUND: Accumulating evidence supports cerebellar involvement in mental disorders, such as schizophrenia, bipolar disorder, depression, anxiety disorders, and attention-deficit/hyperactivity disorder. However, little is known about the cerebellum in developmental stages of these disorders. In particular, whether cerebellar morphology is associated with early expression of specific symptom domains remains unclear.METHODS: We used machine learning to test whether cerebellar morphometric features could robustly predict general cognitive function and psychiatric symptoms in a large and well-characterized developmental community sample centered on adolescence (Philadelphia Neurodevelopmental Cohort, n = 1401, age 8-23 years).RESULTS: Cerebellar morphology was associated with both general cognitive function and general psychopathology (mean correlations between predicted and observed values: r = .20 and r = .13; p CONCLUSIONS: The robust associations with psychiatric symptoms in the age range when these typically emerge highlight the cerebellum as a key brain structure in the development of severe mental disorders.

Supported by histological and genetic evidence implicating myelin, neuroinflammation and oligodendrocyte dysfunction in schizophrenia spectrum disorders (SZ), diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) abnormalities when compared to healthy controls (HC). The diagnostic specificity remains unclear, with bipolar disorders (BD) frequently conceptualized as a less severe clinical manifestation along a psychotic spectrum. Further, the age-related dynamics and possible sex differences of WM abnormalities in SZ and BD are currently understudied.Using tract-based spatial statistics (TBSS) we compared DTI-based microstructural indices between SZ (n=128), BD (n=61), and HC (n=293). We tested for age-by-group and sex-by-group interactions, computed effect sizes within different age-bins and within genders.TBSS revealed global reductions in fractional anisotropy (FA) and increases in radial (RD) diffusivity in SZ compared to HC, with strongest effects in the body and splenium of the corpus callosum, and lower FA in SZ compared to BD in right inferior longitudinal fasciculus and right inferior fronto-occipital fasciculus, and no significant differences between BD and HC. The results were not strongly dependent on age or sex. Despite lack of significant group-by-age interactions, a sliding-window approach supported widespread WM involvement in SZ with most profound differences in FA from the late 20s.

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer’s disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields’ genetic architecture. T1-weighted brain scans (n=21297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, covarying for total hippocampal volume. We further calculated the single nucleotide polymorphism (SNP)-based heritability of twelve subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. All outcome measures were corrected for age, sex, and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from .14 to .27, all p< 1×10-16) and clustered together based on their genetic correlations compared to other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.

BackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.MethodsWe assessed symptoms of depression and anxiety using Beck’s Depression and Beck’s Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.ResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.ConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

Cannabinoid receptor 1 (CNR1) gene polymorphisms have been associated with central and peripheral effects of cannabis and schizophrenia pathophysiology. Here, we have tested whether three CNR1 variants (rs1049353, rs1535255 and rs2023239) are associated with changes in brain volumes, body mass index (BMI) or psychopathological scores in a 3-year longitudinal study of 65 first-episode psychosis patients. The rs1049353 at-risk allele was significantly associated with a greater reduction of caudate volume, and the rs2023239 T/C polymorphism showed a significant decrease in thalamic volume after the 3-year period. For those who were not cannabis users, the rs1535255 and rs2023239 polymorphisms had effects in lateral ventricle (LV), and LV and white matter, respectively. The rs2023239 variant also was associated with significant improvements in positive and negative symptoms of schizophrenia. There was no significant effect of any of the variants on changes in BMI over the 3-year study. Finally, an interaction between all three polymorphisms was found involving evolution of positive symptoms. These findings suggest that the cannabinoid pathway is associated with schizophrenia evolution over time. However, further studies using larger cohorts are needed to confirm these results. If confirmed, the present findings could lead in subsequent investigations for identification of novel drug targets for improved treatment of patients suffering from schizophrenia.

Recent findings indicate that alterations of the amygdalar resting-state fMRI connectivity play an important role in the etiology of depression. While both depression and resting-state brain activity are shaped by genes and environment, the relative contribution of genetic and environmental factors mediating the relationship between amygdalar resting-state connectivity and depression remain largely unexplored. Likewise, novel neuroimaging research indicates that different mathematical representations of resting-state fMRI activity patterns are able to embed distinct information relevant to brain health and disease. The present study analyzed the influence of genes and environment on amygdalar resting-state fMRI connectivity, in relation to depression risk. High-resolution resting-state fMRI scans were analyzed to estimate functional connectivity patterns in a sample of 48 twins (24 monozygotic pairs) informative for depressive psychopathology (6 concordant, 8 discordant and 10 healthy control pairs). A graph-theoretical framework was employed to construct brain networks using two methods: (i) the conventional approach of filtered BOLD fMRI time-series and (ii) analytic components of this fMRI activity. Results using both methods indicate that depression risk is increased by environmental factors altering amygdalar connectivity. When analyzing the analytic components of the BOLD fMRI time-series, genetic factors altering the amygdala neural activity at rest show an important contribution to depression risk. Overall, these findings show that both genes and environment modify different patterns the amygdala resting-state connectivity to increase depression risk. The genetic relationship between amygdalar connectivity and depression may be better elicited by examining analytic components of the brain resting-state BOLD fMRI signals. Hum Brain Mapp 36:3761–3776, 2015. © 2015 Wiley Periodicals, Inc.

BackgroundCortical thickness measurement offers an index of brain development processes. In healthy individuals, cortical thickness is reduced with increasing age and is related to cognitive decline. Cortical thinning has been reported in schizophrenia. Whether cortical thickness changes differently over time in patients and its impact on outcome remain unanswered.MethodData were examined from 109 patients and 76 healthy controls drawn from the Santander Longitudinal Study of first-episode schizophrenia for whom adequate structural magnetic resonance imaging (MRI) data were available (n = 555 scans). Clinical and cognitive assessments and MRIs were acquired at three regular time points during a 3-year follow-up period. We investigated likely progressive cortical thickness changes in schizophrenia during the first 3 years after initiating antipsychotic treatment. The effects of cortical thickness changes on cognitive and clinical variables were also examined along with the impact of potential confounding factors.ResultsThere were significant diagnoses × scan time interaction main effects for total cortical thickness (F1,309.1 = 4.60, p = 0.033) and frontal cortical thickness (F1,310.6 = 5.30, p = 0.022), reflecting a lesser thinning over time in patients. Clinical and cognitive outcome was not associated with progressive cortical changes during the early years of the illness.ConclusionsCortical thickness abnormalities do not unswervingly progress, at least throughout the first years of the illness. Previous studies have suggested that modifiable factors may partly account for cortical thickness abnormalities. Therefore, the importance of implementing practical actions that may modify those factors and improve them over the course of the illness should be highlighted.

Hemispheric asymmetry is a cardinal feature of human brain organization. Altered brain asymmetry has also been linked to some cognitive and neuropsychiatric disorders. Here the ENIGMA consortium presents the largest ever analysis of cerebral cortical asymmetry and its variability across individuals. Cortical thickness and surface area were assessed in MRI scans of 17,141 healthy individuals from 99 datasets worldwide. Results revealed widespread asymmetries at both hemispheric and regional levels, with a generally thicker cortex but smaller surface area in the left hemisphere relative to the right. Regionally, asymmetries of cortical thickness and/or surface area were found in the inferior frontal gyrus, transverse temporal gyrus, parahippocampal gyrus, and entorhinal cortex. These regions are involved in lateralized functions, including language and visuospatial processing. In addition to population-level asymmetries, variability in brain asymmetry was related to sex, age, and brain size (indexed by intracranial volume). Interestingly, we did not find significant associations between asymmetries and handedness. Finally, with two independent pedigree datasets (N = 1,443 and 1,113, respectively), we found several asymmetries showing modest but highly reliable heritability. The structural asymmetries identified, and their variabilities and heritability provide a reference resource for future studies on the genetic basis of brain asymmetry and altered laterality in cognitive, neurological, and psychiatric disorders.Significance StatementLeft-right asymmetry is a key feature of the human brain's structure and function. It remains unclear which cortical regions are asymmetrical on average in the population, and how biological factors such as age, sex and genetic variation affect these asymmetries. Here we describe by far the largest ever study of cerebral cortical brain asymmetry, based on data from 17,141 participants. We found a global anterior-posterior 'torque' pattern in cortical thickness, together with various regional asymmetries at the population level, which have not been previously described, as well as effects of age, sex, and heritability estimates. From these data, we have created an on-line resource that will serve future studies of human brain anatomy in health and disease.

Background:Though cognitive abilities in adulthood are largely influenced by individual genetic background, they have also been shown to be importantly influenced by environmental factors. Some of these influences are mediated by epigenetic mechanisms. Accordingly, polymorphic variants in the epigenetic gene DNMT3B have been linked to neurocognitive performance. Since monozygotic (MZ) twins may show larger or smaller intrapair phenotypic differences depending on whether their genetic background is more or less sensitive to environmental factors, a twin design was implemented to determine if particular polymorphisms in the DNMT3B gene may be linked to a better (worse) response to enriched (deprived) environmental factors.Methods:Applying the variability gene methodology in a sample of 54 healthy MZ twin pairs (108 individuals) with no lifetime history of psychopathology, two DNMT3B polymorphisms were analyzed in relation to their intrapair differences for either intellectual quotient (IQ) or working memory performance.Results:MZ twin pairs with the CC genotype for rs406193 SNP showed statistically significant larger intrapair differences in IQ than CT pairs.Conclusions:Results suggest that DNMT3B polymorphisms may explain variability in the IQ response to either enriched or impoverished environmental conditions. Accordingly, the applied methodology is shown as a potentially valuable tool for determining genetic markers of cognitive plasticity. Further research is needed to confirm this specific result and to expand on other putative genetic markers of environmental sensitivity.

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO) in a small percentage of families. Clinical surveys report an increased prevalence of missense variants in NOTCH1 in family members of individuals with LVOTO and other types of congenital heart disease (CHD). However, the risk conferred by rare variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare missense variants were found in an additional 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and non-coding variants in NOTCH1 in 271 adult cases and 333,571 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO (g.chr9:139427582C>T, Odds Ratio 16.9, p=3.12e-6; g.chr9:139435649C>T, Odds Ratio 19.6, p = 2.44e-6) amongst European-ancestry British individuals. This result was replicated in an independent analysis of 51 cases and 68,901 controls of non-European and mixed ancestry. We conclude that carrying rare predicted loss of function variants or either of two intronic variants in NOTCH1 confer significant risk for LVOTO. Our approach demonstrates the utility of population-based datasets in quantifying the specific risk of individual variants for disease related phenotypes.Author summaryCongenital heart defects are the most common class of birth defect and are present in 1% of live births. Although CHD cases are often clustered in families, and thus the causal variant(s) are seemingly inherited, finding genetic variants causing these defects has been challenging. With the knowledge that variation in the NOTCH1 gene previously has been associated with CHDs affecting the left side of the heart, our aim was to further investigate the role of different types of NOTCH1 variants in left sided CHDs in two cohorts – a cohort of Finnish families with severe lesions affecting the left side of the heart, and the UK Biobank population including individuals with less severe left-sided lesions such as bicuspid aortic valve, congenital aortic stenosis, and coarctation of the aorta. We found a causal loss-of-function NOTCH1 variant in 6% of the families in the Finnish cohort and in the UK Biobank cohort, we identified two rare variants in the non-coding region of NOTCH1, associated with severe left-sided CHDs. These findings support screening of NOTCH1 loss-of-function variants in patients with severe left sided congenital heart defects and suggests that non-coding region variants in NOTCH1 play a role in CHDs.

Among the major psychiatric disorders, anxious-depressive disorders stand out as one of the more prevalent and more frequently associated with hypothalamic-pituitary-adrenal (HPA) axis abnormalities. Methylation at the exon 1

A healthy transition from adolescence to adulthood relies on a continuous individual adaptation to a dynamic environment. Here, we employed data driven multivariate approaches to derive both general cognitive and psychopathology factors as well as brain phenotypes in children and adolescents in the publicly available PNC sample. We identified a distinct brain white matter pattern which proved central for prediction of heritable cognition and psychopathology scores, highlighting the importance of fronto-temporal connections for intellectual and mental development.

Purpose:To test whether firstly, different parental rearing components were associated with different dimensions of psychiatric symptoms in adulthood, secondlyBDNF-Val66Met polymorphism moderated this association and thirdly, this association was due to genetic confounding.Method:Perceived parental rearing according to Parental Bonding Instrument (PBI), psychiatric symptoms evaluated with the Brief Symptom Inventory (BSI) and theBDNF-Val66Met polymorphism were analyzed in a sample of 232 adult twins from the general population.Results:In the whole sample, paternal care was negatively associated with depression. Maternal overprotection was positively associated with paranoid ideation, obsession-compulsion and somatization. Gene-environment interaction effects were detected between theBDNF-Val66Met polymorphism and maternal care on phobic anxiety, paternal care on hostility, maternal overprotection on somatization and paternal overprotection also in somatization. In the subsample of MZ twins, intrapair differences in maternal care were associated with anxiety, paranoid ideation and somatization.Conclusions:Met carriers were, in general, more sensitive to the effects of parental rearing compared to Val/Val carriers in relation to anxiety and somatization. Contra-intuitively, our findings suggest that high rates of maternal care might be of risk for Met carriers regarding anxiety. Results from analyses controlling for genetic confounding were in line with this finding.

As findings on the neuropathological and behavioral components of Alzheimer’s disease (AD) continue to accrue, converging evidence suggests that macroscale brain functional disruptions may mediate their association. Recent developments on theoretical neuroscience indicate that instantaneous patterns of brain connectivity and metastability may be a key mechanism in neural communication underlying cognitive performance. However, the potential significance of these patterns across the AD spectrum remains virtually unexplored. We assessed the clinical sensitivity of static and dynamic functional brain disruptions across the AD spectrum using resting-state fMRI in a sample consisting of AD patients (n = 80) and subjects with either mild (n = 44) or subjective (n = 26) cognitive impairment (MCI, SCI). Spatial maps constituting the nodes in the functional brain network and their associated time-series were estimated using spatial group independent component analysis and dual regression, and whole-brain oscillatory activity was analyzed both globally (metastability) and locally (static and dynamic connectivity). Instantaneous phase metrics showed functional coupling alterations in AD compared to MCI and SCI, both static (putamen, dorsal and default-mode) and dynamic (temporal, frontal-superior and default-mode), along with decreased global metastability. The results suggest that brains of AD patients display altered oscillatory patterns, in agreement with theoretical premises on cognitive dynamics.

Recent efforts using diffusion tensor imaging (DTI) have documented white matter (WM) alterations in Alzheimer’s disease (AD). The full potential of whole-brain DTI, however, has not been fully exploited as studies have focused on individual microstructural indices independently. In patients with AD (n = 79), mild (MCI, n = 55) and subjective (SCI, n = 30) cognitive impairment, we applied linked independent component analysis (LICA) to model inter-subject variability across five complementary DTI measures (fractional anisotropy (FA), axial/radial/mean diffusivity, diffusion tensor mode), two crossing fiber measures estimated using a multi-compartment crossing-fiber model reflecting the volume fraction of the dominant (f1) and non-dominant (f2) diffusion orientation, and finally, connectivity density obtained from full-brain probabilistic tractography. The LICA component explaining the largest data variance was highly sensitive to disease severity (AD

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Many mental disorders emerge during adolescence, which may reflect a cost of the potential for brain plasticity offered during this period. Brain dysconnectivity has been proposed as a common factor across diagnostic categories.To investigate the hypothesis that brain dysconnectivity is a transdiagnostic phenotype in adolescence with increased susceptibility and symptoms of psychiatric disease.We investigated clinical symptoms as well as cognitive function in 6487 individuals aged 8 to 21 years from November 1, 2009, to November 30, 2011, in the Philadelphia Neurodevelopmental Cohort and analyzed diffusion magnetic resonance imaging brain scans for 748 of the participants.Independent component analysis was used to derive dimensional psychopathology scores, and genome-wide complex trait analysis was used to estimate its heritability. Multimodal fusion simultaneously modeled contributions of the diffusion magnetic resonance imaging metrics fractional anisotropy, mean diffusivity, radial diffusivity, L1 (the principal diffusion tensor imaging eigen value), mode of anisotropy, as well as dominant and secondary fiber orientations, and structural connectivity density, and their association with general psychopathology and cognition.Machine learning with 10-fold cross-validation and permutation testing in 729 individuals (aged 8 to 22 years; mean [SD] age, 15.1 [3.3] years; 343 females [46%]) revealed significant association with general psychopathology levels (r = 0.24, P .001) and cognition (r = 0.39, P .001). A brain white matter pattern reflecting frontotemporal connectivity and crossing fibers in the uncinate fasciculus was the most associated feature for both traits. Univariate analysis across a range of clinical domains and cognitive test scores confirmed its transdiagnostic importance. Both the general psychopathology (16%; SE, 0.095; P = .05) and cognitive (18%; SE, 0.09; P = .01) factor were heritable and showed a negative genetic correlation.Dimensional and heritable general cognitive and psychopathology factors are associated with specific patterns of white matter properties, suggesting that dysconnectivity is a transdiagnostic brain-based phenotype in individuals with increased susceptibility and symptoms of psychiatric disorders.

Hosting nearly eighty percent of all human neurons, the cerebellum is functionally connected to large regions of the brain. Accumulating data suggest that some cerebellar resting-state alterations may constitute a key candidate mechanism for depressive psychopathology. While there is some evidence linking cerebellar function and depression, two topics remain largely unexplored. First, the genetic or environmental roots of this putative association have not been elicited. Secondly, while different mathematical representations of resting-state fMRI patterns can embed diverse information of relevance for health and disease, many of them have not been studied in detail regarding the cerebellum and depression. Here, high-resolution fMRI scans were examined to estimate functional connectivity patterns across twenty-six cerebellar regions in a sample of 48 identical twins (24 pairs) informative for depression liability. A network-based statistic approach was employed to analyze cerebellar functional networks built using three methods: the conventional approach of filtered BOLD fMRI time-series, and two analytic components of this oscillatory activity (amplitude envelope and instantaneous phase). The findings indicate that some environmental factors may lead to depression vulnerability through alterations of the neural oscillatory activity of the cerebellum during resting-state. These effects may be observed particularly when exploring the amplitude envelope of fMRI oscillations. Acknowledgements: We are indebted to the Medical Image core facility of the Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) for the technical help. L.F. was supported by the Spanish SAF2008-05674-C03-01, the European Twins Study Network on Schizophrenia Research Training Network (grant number EUTwinsS, MRTN-CT-2006-035987), the Catalan 2014SGR1636 and the Ministry of Science and Innovation (PIM2010ERN-00642) in frame of ERA-NET NEURON. L.F., N.B., P.B., B. C.-F. and A.C.-P. were supported by the Spanish Ministry of Science and Innovation (ES-EUEpiBrain, Grant SAF 2015-71526-REDT). G.D. was supported by the ERC Advanced Grant DYSTRUCTURE (n. 295129), by the FET Flagship Human Brain Project (n. 604102), by the Spanish Research Project PSI2013-42091, by the FP7-ICT BrainScaleS (n. 269921) and CORONET (n. 269459) and by EraNet Neuron SEMAINE (PCIN-2013-026). P.B. was partially supported by The Bial Foundation (Grant 262/2012). We are also grateful to all the participants, to Ximena Goldberg and Silvia Alemany for their contribution to sample collection, and to the MRI technicians César Garrido and Santi Sotés for their technical assistance.

Hosting nearly eighty percent of all human neurons, the cerebellum is functionally connected to large regions of the brain. Accumulating data suggest that some cerebellar resting-state alterations may constitute a key candidate mechanism for depressive psychopathology. While there is some evidence linking cerebellar function and depression, two topics remain largely unexplored. First, the genetic or environmental roots of this putative association have not been elicited. Secondly, while different mathematical representations of resting-state fMRI patterns can embed diverse information of relevance for health and disease, many of them have not been studied in detail regarding the cerebellum and depression. Here, high-resolution fMRI scans were examined to estimate functional connectivity patterns across twenty-six cerebellar regions in a sample of 48 identical twins (24 pairs) informative for depression liability. A network-based statistic approach was employed to analyze cerebellar functional networks built using three methods: the conventional approach of filtered BOLD fMRI time-series, and two analytic components of this oscillatory activity (amplitude envelope and instantaneous phase). The findings indicate that some environmental factors may lead to depression vulnerability through alterations of the neural oscillatory activity of the cerebellum during resting-state. These effects may be observed particularly when exploring the amplitude envelope of fMRI oscillations.

Background Most imaging genetic studies on psychosis have relied upon conventional quantitative trait locus (QTL) analysis, which examines whether distinct allelic variants in a DNA locus are associated with the mean difference in the quantitative measure of a phenotypic trait, e.g. mean brain volumes. However, alternative genotypes can also moderate the variance heterogeneity of a phenotype, which is frequently referred to as variance-controlling QTL (vQTL). This type of genetic control has been observed across several species and traits, and is usually associated to genetic sensitivity to environmental fluctuation. This is highly relevant for an application in imaging genetics in light of the influential diathesis-stress model of psychopathology. To the best of our knowledge, no previous study has evaluated the presence of vQTLs for brain morphology. Methods Participants: The sample analyzed so far consists of 504 individuals recruited through the ongoing Thematically Organized Psychosis (TOP) study, which includes 216 healthy controls (HC), and 288 patients with psychosis (SZ: 143, BD: 145). Patients were diagnosed using the structured clinical interview for DSM-IV (SCID) by trained clinicians. Genetic data: The participants were genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 (Affymetrix Inc, Santa Clara, CA, USA). Quality control was performed using PLINK. Brain imaging: Magnetic resonance imaging (MRI) structural data were acquired using a 3D T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence, and the T1-weighted scans were processed using FreeSurfer, to obtain estimated of cortical thickness/area/volume and subcortical volumes. vQTL assessment: The relationship between genome-wide single nucleotide polymorphism (SNP) variants and the phenotypic variance of each brain feature of interest was assessed by means of double generalized linear models, and multiple testing adjustments were later performed. Results Exploratory analyses revealed no significant between-group differences in variance for the brain features considered. Genome-wide vQTL analyses indicated that the population variance of features such as cerebellar volume and temporal pole cortical thickness could be under partial genetic control. Replication of these findings and diagnose-genotype interaction tests are currently being implemented and are expected to be competed by early autumn 2016. Discussion The findings suggest that inter-individual variability in brain morphometry may be influenced by specific genetic variants. Carriers of those genotypes may be particularly susceptible to brain deficits and psychopathology after exposure to pathogenic environments and, simultaneously, have increased likelihood of benefiting from enriched/non-adverse experiences.

Late and early stressful factors have widely been recognized to play a role in the aetiology of depression. Recent research indicates that such adverse environmental stimuli may alter gene expression in humans via epigenetic modifications. While epigenetic changes such as DNA methylation are likely involved in these processes, it is still unknown what specific genomic loci may be hyper- or hypo-methylated in depression. The association between depressive symptoms during the last 30 days (Brief Symptom Inventory [BSI]) and peripheral-blood DNA methylation levels at genomic loci previously reported as epigenetically altered in saliva and brain of depressive patients was evaluated in a community sample of 34 adult Caucasian MZ twins (17 pairs). Intrapair DNA methylation differences in an intron of DEPDC7 (chr11:33040743) were associated with intrapair differences in current depressive symptoms. Accordingly, a site-specific 10% DNA hypomethylation in a co-twin would correlate with a current depressive symptom score around 3.1 BSI points above the score of his/her less-depressed co-twin. These findings indicate that DEPDC7 hypomethylation in peripheral blood DNA may be associated with recent depressive symptomatology, in line with previous results.

Recent findings indicate that alterations of the amygdalar resting‐state fMRI connectivity play an important role in the etiology of depression. While both depression and resting‐state brain activity are shaped by genes and environment, the relative contribution of genetic and environmental factors mediating the relationship between amygdalar resting‐state connectivity and depression remain largely unexplored. Likewise, novel neuroimaging research indicates that different mathematical representations of resting‐state fMRI activity patterns are able to embed distinct information relevant to brain health and disease. The present study analyzed the influence of genes and environment on amygdalar resting‐state fMRI connectivity, in relation to depression risk. High‐resolution resting‐state fMRI scans were analyzed to estimate functional connectivity patterns in a sample of 48 twins (24 monozygotic pairs) informative for depressive psychopathology (6 concordant, 8 discordant and 10 healthy control pairs). A graph‐theoretical framework was employed to construct brain networks using two methods: (i) the conventional approach of filtered BOLD fMRI time‐series and (ii) analytic components of this fMRI activity. Results using both methods indicate that depression risk is increased by environmental factors altering amygdalar connectivity. When analyzing the analytic components of the BOLD fMRI time‐series, genetic factors altering the amygdala neural activity at rest show an important contribution to depression risk. Overall, these findings show that both genes and environment modify different patterns the amygdala resting‐state connectivity to increase depression risk. The genetic relationship between amygdalar connectivity and depression may be better elicited by examining analytic components of the brain resting‐state BOLD fMRI signals. Hum Brain Mapp 36:3761–3776, 2015. © 2015 Wiley Periodicals, Inc.

Prenatal stress has been widely associated with a number of short- and long-term pathological outcomes. Epigenetic mechanisms are thought to partially mediate these environmental insults into the fetal physiology. One of the main targets of developmental programming is the hypothalamic-pituitary-adrenal (HPA) axis as it is the main regulator of the stress response. Accordingly, an increasing number of researchers have recently focused on the putative association between DNA methylation at the glucocorticoid receptor gene (NR3C1) and prenatal stress, among other types of psychosocial stress. The current study aims to systematically review and meta-analyze the existing evidence linking several forms of prenatal stress with DNA methylation at the region 1F of the NR3C1 gene. The inclusion of relevant articles allowed combining empirical evidence from 977 individuals by meta-analytic techniques, whose methylation assessments showed overlap across 5 consecutive CpG sites (GRCh37/hg19 chr5:142,783,607-142,783,639). From this information, methylation levels at CpG site 36 displayed a significant correlation to prenatal stress (r = 0.14, 95% CI: 0.05-0.23, P = 0.002). This result supports the proposed association between a specific CpG site located at the NR3C1 promoter and prenatal stress. Several confounders, such as gender, methylation at other glucocorticoid-related genes, and adjustment for pharmacological treatments during pregnancy, should be taken into account in further studies.

In the behavioral sciences, the concept of phenotypic plasticity can be roughly categorized into two classes: developmental and activational plasticity. Developmental plasticity denotes the capacity of an individual carrying a specific genetic background to adopt different developmental trajectories under distinct settings. Complementarily, activational plasticity refers to the differential activation of adaptation mechanisms: an individual with high activational plasticity would be able to detect a wide range of environments, and to respond to it using a psychobiological phenotype from a relatively large catalogue. In this context, it is feasible postulating that several etiopathogenic mechanisms of depression-related phenotypes can be clarified by expanding on processes of biobehavioral plasticity in response to the experience. This expansion can be elaborated on the basis of both neurodevelopmental phenomena (developmental plasticity) and novel biological mechanisms detectable through neuroimaging and epigenetics approaches (activational plasticity). The present work expands on two specific hypotheses. First, depression-related psychopathological phenotypes are induced by factors altering the early neurodevelopment, and these long-lasting changes can be assessed in adulthood (depression and developmental plasticity). Secondly, the clinical manifestation of depression-related psychopathological phenotypes can be understood as activational plasticity deficits; these deficits can be assessed as neurobiological disease traits using novel epigenetic and neuroimaging techniques (depression and activational plasticity). The results of this work provide support to the neuroplasticity hypothesis of depression, from both developmental and activational perspectives. Developmentally, they suggest putative etiopathogenic pathways leading from an altered early neurodevelopment to an increased risk for depression-related phenotypes. By exploring and combining genetic, environmental and psychopathologic concepts, the feasibility of these results has been explained by combining the popular genetic pleiotropy hypothesis in psychiatry with a notion of disease-specificity liability driven by the environment. With regards to activational plasticity, this work has proposed novel genetic and epigenetic signatures potentially underlying the clinical manifestation of neuropsychiatric and neurocognitive features of depression (i.e., the genetics of DNMT3B and the epigenetics of DEPDC7); additionally, it has proposed new putative neurobiological mechanisms to explain depressive traits (i.e., a combination of differential and variable methylation, a genetically-mediated hippocampal communication deficit, and a new amygdalar synchrony failure driven by the genes).

Depressive disorders have been shown to be highly influenced by environmental pathogenic factors, some of which are believed to exert stress on human brain functioning via epigenetic modifications. Previous genome-wide methylomic studies on depression have suggested that, along with differential DNA methylation, affected co-twins of monozygotic (MZ) pairs have increased DNA methylation variability, probably in line with theories of epigenetic stochasticity. Nevertheless, the potential biological roots of this variability remain largely unexplored. The current study aimed to evaluate whether DNA methylation differences within MZ twin pairs were related to differences in their psychopathological status. Data from the Illumina Infinium HumanMethylation450 Beadchip was used to evaluate peripheral blood DNA methylation of 34 twins (17 MZ pairs). Two analytical strategies were used to identify (a) differentially methylated probes (DMPs) and (b) variably methylated probes (VMPs). Most DMPs were located in genes previously related to neuropsychiatric phenotypes. Remarkably, one of these DMPs (cg01122889) was located in the WDR26 gene, the DNA sequence of which has been implicated in major depressive disorder from genome-wide association studies. Expression of WDR26 has also been proposed as a biomarker of depression in human blood. Complementarily, VMPs were located in genes such as CACNA1C, IGF2 and the p38 MAP kinase MAPK11, showing enrichment for biological processes such as glucocorticoid signaling. These results expand on previous research to indicate that both differential methylation and differential variability have a role in the etiology and clinical manifestation of depression, and provide clues on specific genomic loci of potential interest in the epigenetics of depression.

Neurodevelopmental disruptions caused by obstetric complications play a role in the etiology of several phenotypes associated with neuropsychiatric diseases and cognitive dysfunctions. Importantly, it has been noticed that epigenetic processes occurring early in life may mediate these associations. Here, DNA methylation signatures at IGF2 (insulin-like growth factor 2) and IGF2BP1-3 (IGF2-binding proteins 1-3) were examined in a sample consisting of 34 adult monozygotic (MZ) twins informative for obstetric complications and cognitive performance. Multivariate linear regression analysis of twin data was implemented to test for associations between methylation levels and both birth weight (BW) and adult working memory (WM) performance. Familial and unique environmental factors underlying these potential relationships were evaluated. A link was detected between DNA methylation levels of two CpG sites in the IGF2BP1 gene and both BW and adult WM performance. The BW-IGF2BP1 methylation association seemed due to non-shared environmental factors influencing BW, whereas the WM-IGF2BP1 methylation relationship seemed mediated by both genes and environment. Our data is in agreement with previous evidence indicating that DNA methylation status may be related to prenatal stress and later neurocognitive phenotypes. While former reports independently detected associations between DNA methylation and either BW or WM, current results suggest that these relationships are not confounded by each other. This work is supported by the Spanish SAF2008-05674-C03-01, European Twins Study Network on Schizophrenia Research Training Network (grant number EUTwinsS; MRTN-CT-2006-035987; local PIs: F.L. and N.I.), the Catalan 2014SGR1636 and the Ministry of Science and Innovation (PIM2010ERN-00642) in frame of ERA-NET NEURON. A.C.P. was funded by CONACyT (Mexico). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Season of birth has been shown to influence risk for several neuropsychiatric diseases. Furthermore, it has been suggested that season of birth modifies a number of brain morphological traits. Since cortical thickness alterations have been reported across some levels of the psychosis-spectrum, this study was aimed at i) assessing the scarcely explored relationship between cortical thickness and severity of subclinical psychotic experiences (PEs) in healthy subjects, and ii) evaluating the potential impact of season of birth in the preceding thickness-PEs relationship. As both PEs and brain cortical features are heritable, the current work used monozygotic twins to separately evaluate familial and unique environmental factors. High-resolution structural MRI scans of 48 twins (24 monozygotic pairs) were analyzed to estimate cortical thickness using FreeSurfer. They were then examined in relation to PEs, accounting for the effects of birth season; putative differential relationships between PEs and cortical thickness depending on season of birth were also tested. Current results support previous findings indicative of cortical thickening in healthy individuals with high psychometrically assessed psychosis scores, probably in line with theories of compensatory aspects of brain features in non-clinical populations. Additionally, they suggest distinct patterns of cortical thickness-PEs relationships depending on birth seasonality. Familial factors underlying the presence of PEs may drive these effects.

Epigenetics is the study of gene expression changes that are produced by heritable, though potentially reversible, modifications of chromatin structure or DNA methylation. DNA methylation is interesting in epidemiological studies, due to its accessibility and since previous evidence indicates that large inter-individual differences in methylation levels at some loci may correlate with phenotypic plasticity in changing environments.Prior genome-wide methylomic research on depression has suggested that, together with differential DNA methylation changes, affected co-twins of monozygotic twin pairs have increased DNA methylation variability, probably in line with theories of epigenetic stochasticity. However, the putative biological roots of this variability remain largely unexplored.This study evaluate whether DNA methylation differences within MZ twin pairs were related to differences in their depressive status. Genome-wide DNA methylation levels were measured in peripheral blood of 34 twins (17 MZ pairs) using Illumina Infinium Human Methylation450 Beadchip. Two analytical strategies were used to identify differentially methylated probes (DMPs) and variably methylated probes (VMPs).The majority of the DMPs were located in genes previously related to neuropsychiatric phenotypes, such as WDR26, a GWAS hit for MDD whose expression levels have been found altered in blood of depressed individuals.VMPs were located in genes such as CACNA1C, IGF2 and the p38 MAP kinase MAPK11, showing enrichment for biological processes such as glucocorticoid signaling.The findings expand on previous research to indicate that both differential and variable methylation may play a role in the etiopathology of depression, and suggest specific genomic loci of potential interest in the epigenetics of depression.Disclosure of interestThe authors have not supplied their declaration of competing interest.

IntroductionDespite the fact that depression has an estimated heritability around 40%, there is no definitive candidate gene contributing to its etiology. The lack of an identified genetic component for high-heritability disorders, like depression, gave rise to the concept of missing heritability. The epigenetics’ field has pushed forward new hypotheses to fill this gap since transgenerational inheritance of epigenetic patterns has been described both in animal models and, more recently, in humans. Depression is usually associated with an abnormal stress response and an altered hypothalamic-pituitary-adrenal axis, regulated by the glucocorticoid receptor (coded by NR3C1 gene). Therefore, NR3C1 has been widely investigated as a functional candidate gene involved in anxious-depressive spectrum disorders (ADSD) although a more comprehensive study of its methylation is further required (Palma-Gudiel et al., 2015).AimsTo analyze NR3C1 promoter's methylation and to study its association with anxious-depressive spectrum disorders.MethodsThe sample consisted of 48 pairs of monozygotic twins, from the UB twin register, grouped as concordant, discordant and healthy pairs depending on whether both, one or none of the co-twins of each pair were affected by a lifetime ADSD, according to DSM-IV criteria (SCID). DNA methylation was assessed by bisulfite conversion and subsequent pyrosequencing.ResultsHypermethylation at specific CpG sites, not previously reported, was detected in concordant twin pairs as compared with discordant and healthy groups (P = 0.03).ConclusionsThe epigenetic pattern newly described in NR3C1 gene may be contributing to the familial component of depression and thus could be putatively explained by transgenerational inheritance of epigenetic phenomena.Disclosure of interestThe authors have not supplied their declaration of competing interest.

BACKGROUND: Previous research suggests that low birth weight (BW) induces reduced brain cortical surface area (SA) which would persist until at least early adulthood. Moreover, low BW has been linked to psychiatric disorders such as depression and psychological distress, and to altered neurocognitive profiles. AIMS: We present novel findings obtained by analysing high-resolution structural MRI scans of 48 twins; specifically, we aimed: i) to test the BW-SA association in a middle-aged adult sample; and ii) to assess whether either depression/anxiety disorders or intellectual quotient (IQ) influence the BW-SA link, using a monozygotic (MZ) twin design to separate environmental and genetic effects. RESULTS: Both lower BW and decreased IQ were associated with smaller total and regional cortical SA in adulthood. Within a twin pair, lower BW was related to smaller total cortical and regional SA. In contrast, MZ twin differences in SA were not related to differences in either IQ or depression/anxiety disorders. CONCLUSION: The present study supports findings indicating that i) BW has a long-lasting effect on cortical SA, where some familial and environmental influences alter both foetal growth and brain morphology; ii) uniquely environmental factors affecting BW also alter SA; iii) higher IQ correlates with larger SA; and iv) these effects are not modified by internalizing psychopathology. This work was supported by the Spanish SAF2008-05674, European Twins Study Network on Schizophrenia Research Training Network (grant number EUTwinsS; MRTN-CT-2006-035987), the Catalan 2014SGR1636 and the PIM2010-ERN- 00642 in frame of ERA-NET NEURON. A. Córdova- Palomera was funded by The National Council for Science and Technology (CONACyT, Mexico). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.