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Significance Rapid impressions of trustworthiness can have extreme consequences, impacting financial lending, partner selection, and death-penalty sentencing decisions. But to what extent do people disagree about who looks trustworthy, and why? Here, we demonstrate that individual differences in trustworthiness and other impressions are substantial and stable, agreeing with the classic idea that social perception can be influenced in part by the “eye of the beholder.” Moreover, by examining twins, we show that individual differences in impressions of trustworthiness are shaped mostly by personal experiences, instead of genes or familial experiences. Our study highlights individual social learning as a key mechanism by which we individually come to trust others, with potentially profound consequences for everyday trust decisions., People evaluate a stranger’s trustworthiness from their facial features in a fraction of a second, despite common advice “not to judge a book by its cover.” Evaluations of trustworthiness have critical and widespread social impact, predicting financial lending, mate selection, and even criminal justice outcomes. Consequently, understanding how people perceive trustworthiness from faces has been a major focus of scientific inquiry, and detailed models explain how consensus impressions of trustworthiness are driven by facial attributes. However, facial impression models do not consider variation between observers. Here, we develop a sensitive test of trustworthiness evaluation and use it to document substantial, stable individual differences in trustworthiness impressions. Via a twin study, we show that these individual differences are largely shaped by variation in personal experience, rather than genes or shared environments. Finally, using multivariate twin modeling, we show that variation in trustworthiness evaluation is specific, dissociating from other key facial evaluations of dominance and attractiveness. Our finding that variation in facial trustworthiness evaluation is driven mostly by personal experience represents a rare example of a core social perceptual capacity being predominantly shaped by a person’s unique environment. Notably, it stands in sharp contrast to variation in facial recognition ability, which is driven mostly by genes. Our study provides insights into the development of the social brain, offers a different perspective on disagreement in trust in wider society, and motivates new research into the origins and potential malleability of face evaluation, a critical aspect of human social cognition.

Contains fulltext : 248656.pdf (Publisher’s version ) (Closed access) BACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. RESULTS: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10(-8)), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10(-6)) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10(-7); rs73033497, p = 8.8 × 10(-7); rs7914279, p = 6.4 × 10(-7)), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10(-7)) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10(-7)), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10(-7)) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). CONCLUSIONS: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.

BACKGROUNDSex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.METHODSWe conducted the largest to date genome-wide genotype–by–sex (GxS) interaction of risk for these disorders, using 85,735 cases (33,403 SCZ, 19,924 BIP, 32,408 MDD) and 109,946 controls from the Psychiatric Genomics Consortium (PGC) and iPSYCH.RESULTSAcross disorders, genome-wide significant SNP-by-sex interaction was detected for a locus encompassingNKAIN2(rs117780815;p=3.2×10−8), that interacts with sodium/potassium-transporting ATPase enzymes implicating neuronal excitability. Three additional loci showed evidence (p−6) for cross-disorder GxS interaction (rs7302529,p=1.6×10−7; rs73033497,p=8.8×10−7; rs7914279,p=6.4×10−7) implicating various functions. Gene-based analyses identified GxS interaction across disorders (p=8.97×10−7) with transcriptional inhibitorSLTM. Most significant in SCZ was aMOCOSgene locus (rs11665282;p=1.5×10−7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509;p=1.1×10−7) in a locus containingIDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant GxS of genes regulating vascular endothelial growth factor (VEGF) receptor signaling in MDD (pFDRCONCLUSIONSIn the largest genome-wide GxS analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development, immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway enrichment levels.

BackgroundThe origin of sex differences in prevalence and presentation of neuropsychiatric and behavioral traits is largely unknown. Given established genetic contributions and correlations across these traits, we tested for a sex-differentiated genetic architecture within and between traits.MethodsUsing genome-wide association study (GWAS) summary statistics for 20 neuropsychiatric and behavioral traits, we tested for differences in SNP-based heritability (h2) and genetic correlation (rgResultsWith current sample sizes (and power), we found no significant, consistent sex differences in SNP-based h2. Between-sex, within-trait genetic correlations were consistently high, although significantly less than 1 for educational attainment and risk-taking behavior. We identified genome-wide significant genes with sex-differentiated effects for eight traits. Several trait pairs shared sex-differentiated effects. The top 0.1% of genes with sex-differentiated effects across traits overlapped with neuron- and synapse-related gene sets. Most between-trait genetic correlation estimates were similar across sex, with several exceptions (e.g. educational attainment & risk-taking behavior).ConclusionsSex differences in the common autosomal genetic architecture of neuropsychiatric and behavioral phenotypes are small and polygenic, requiring large sample sizes. Genes with sex-differentiated effects are enriched for neuron-related gene sets. This work motivates further investigation of genetic, as well as environmental, influences on sex differences.

Introduction Abnormalities in the corpus callosum (CC) and the lateral ventricles (LV) are hallmark features of schizophrenia. These abnormalities have been reported in chronic and in first episode schizophrenia (FESZ). Here we explore further associations between CC and LV in FESZ using diffusion tensor imaging (DTI). Methods . Sixteen FESZ patients and 16 healthy controls (HC), matched on age, gender, and handedness participated in the study. Diffusion and structural imaging scans were acquired on a 3T GE Signa magnet. Volumetric measures for LV and DTI measures for five CC subdivisions were completed in both groups. In addition, two-tensor tractography, the latter corrected for free-water (FAt), was completed for CC. Correlations between LV and DTI measures of the CC were examined in both groups, while correlations between DTI and clinical measures were examined in only FESZ. Results Results from two-tensor tractography demonstrated decreased FAt and increased trace and radial diffusivity (RDt) in the five CC subdivisions in FESZ compared to HC. Central CC diffusion measures in FESZ were significantly correlated with volume of the LV, i.e., decreased FAt values were associated with larger LV volume, while increased RDt and trace values were associated with larger LV volume. In controls, correlations were also significant, but they were in the opposite direction from FESZ. In addition, decreased FAt in FESZ was associated with more positive symptoms. Discussion Partial volume corrected FAt, RDt, and trace abnormalities in the CC in FESZ suggest possible de- or dys-myelination, or changes in axonal diameters, all compatible with neurodevelopmental theories of schizophrenia. Correlational findings between the volume of LV and diffusion measures in FESZ reinforce the concept of a link between abnormalities in the LV and CC in early stages of schizophrenia and are also compatible with neurodevelopmental abnormalities in this population.

Background Cognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases. Methods We combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases. Results PRS for both population IQ (P = 4.39 × 10–28) and EA (P = 1.27 × 10–26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS. Conclusions Cognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) reflects regulation of regional cerebral blood flow in response to neuronal activation. The HRF varies significantly between individuals. This study investigated the genetic contribution to individual variation in HRF using fMRI data from 125 monozygotic (MZ) and 149 dizygotic (DZ) twin pairs. The resemblance in amplitude, latency, and duration of the HRF in six regions in the frontal and parietal lobes was compared between MZ and DZ twin pairs. Heritability was estimated using an ACE (Additive genetic, Common environmental, and unique Environmental factors) model. The genetic influence on the temporal profile and amplitude of HRF was moderate to strong (24%-51%). The HRF may be used in the genetic analysis of diseases with a cerebrovascular etiology.

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach “Accelerated Permutation Inference for the ACE Model (APACE)” where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.

Background: Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. Methods: We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. Results: Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p < 1 × 10− 10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. Conclusions: The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of > 10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia. We are grateful for the support of all study staff and participants. Acknowledgements for each sample are provided in the Supplementary Materials. Data processing and analyses (of the legacy data) at the central site was supported by the National Institute of Mental Health (NIMH) of the National Institutes of Health (NIH) grant number R01MH092380 to T.L.P. supporting the Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium, and NIMH grant R21MH109819 to E.D.R. Appendix A

Background Sex differences are pervasive in psychiatric disorders, including Major Depressive Disorder (MDD), Schizophrenia (SZ), and Bipolar Disorder (BD). MDD is more prevalent in females; SZ more prevalent in males. BD prevalence is similar by sex, but age at onset, illness course, and prognosis vary considerably by sex, for all three disorders. Although the direction of sex effects in prevalence differ, there may be shared sex effects in genetic risk, given shared sex differences in brain abnormalities across these disorders. This is not surprising given previous findings on cross-disorder genetic risk in the Psychiatric Genomics Consortium (PGC) of SZ, BD, and MDD cohorts. Thus using the PGC data we performed a genome-wide analysis of SNP-by-sex interactions. Methods 30,608 SZ patients (65% male) and 38,441 controls (50% male), 18,958 BD patients (65% male) and 29,996 controls (65% male), and 15,961 MDD patients (33% male) and 24,923 controls (49% male) were included. SZ data include 7.6% East Asian (EAS) subjects; all others are of European (EUR) ancestry. Analyses included and excluded EAS subjects. The PGC already described sample acquisition, genotyping, quality control, and 1000 Genomes imputation. Cohort-specific genome-wide association analyses were performed using linear regression in PLINK with a main effect for each SNP, SNP-by-sex interaction terms, and using an additive model. Ancestry principal components were included to control for population stratification. SNPs with poor imputation quality (IMPUTE2 INFO score Results One locus on Chromosome 8 showed genome-wide significant evidence for SNP-by-sex interaction across disorders (EUR: p=3.7×10^−8) with the most significant marker being rs80198067, intronic to the ANKRD46 gene. The association was driven by SZ (p=1.6×10^−4) and MDD (p=8.7×10^−4), not BD (p=1.5×10^−2). ANKRD46 encodes a protein containing multiple ankyrin repeat domains that function in protein-protein interactions in a variety of cellular processes. ANKRD46 is highly expressed in the brain (GTEx; www.gtexportal.org), particularly in frontal cortex. Additionally, several loci showed suggestive (p Discussion Phenotypic sex differences in brain function and structure are commonly observed in SZ, MDD, and BD. We identified one locus with genome-significant evidence for SNP-by-sex interaction across SZ, BD, and MDD. The locus contains the ANKRD46 gene, which is most strongly expressed in the frontal cortex, similar areas of which are abnormal structurally and functionally in SZ and MDD. Further, the shared sex-by-genotype interaction effect on risk of SZ and MDD, but not BD, is consistent with stronger sex differences in prevalence for these disorders than for BD. Future investigation of this locus will be important for understanding the interactions between sex, genes, and brain pathophysiology that cross psychiatric disorders.

Brain masking of MRI images separates brain from surrounding tissue and its accuracy is important for further imaging analyses. We implemented a new brain masking technique based on multi-atlas brain segmentation (MABS) and compared MABS to masks generated using FreeSurfer (FS; version 5.3), Brain Extraction Tool (BET), and Brainwash, using manually defined masks (MM) as the gold standard. We further determined the effect of different masking techniques on cortical and subcortical volumes generated by FreeSurfer. METHODS Images were acquired on a 3-Tesla MR Echospeed system General Electric scanner on five control and five schizophrenia subjects matched on age, sex, and IQ. Automated masks were generated from MABS, FS, BET, and Brainwash, and compared to MM using these metrics: a) volume difference from MM; b) Dice coefficients; and c) intraclass correlation coefficients. RESULTS Mean volume difference between MM and MABS masks was significantly less than the difference between MM and FS or BET masks. Dice coefficient between MM and MABS was significantly higher than Dice coefficients between MM and FS, BET, or Brainwash. For subcortical and left cortical regions, MABS volumes were closer to MM volumes than were BET or FS volumes. For right cortical regions, MABS volumes were closer to MM volumes than were BET volumes. CONCLUSIONS Brain masks generated using FreeSurfer, BET, and Brainwash are rapidly obtained, but are less accurate than manually defined masks. Masks generated using MABS, in contrast, resemble more closely the gold standard of manual masking, thereby offering a rapid and viable alternative.

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23 ± 2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h(2) (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤ 15%), and when global signal regression was implemented, heritability estimates decreased substantially h(2) (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

SummaryAlthough certain characteristics of human faces are broadly considered more attractive (e.g., symmetry, averageness), people also routinely disagree with each other on the relative attractiveness of faces. That is, to some significant degree, beauty is in the “eye of the beholder.” Here, we investigate the origins of these individual differences in face preferences using a twin design, allowing us to estimate the relative contributions of genetic and environmental variation to individual face attractiveness judgments or face preferences. We first show that individual face preferences (IP) can be reliably measured and are readily dissociable from other types of attractiveness judgments (e.g., judgments of scenes, objects). Next, we show that individual face preferences result primarily from environments that are unique to each individual. This is in striking contrast to individual differences in face identity recognition, which result primarily from variations in genes [1]. We thus complete an etiological double dissociation between two core domains of social perception (judgments of identity versus attractiveness) within the same visual stimulus (the face). At the same time, we provide an example, rare in behavioral genetics, of a reliably and objectively measured behavioral characteristic where variations are shaped mostly by the environment. The large impact of experience on individual face preferences provides a novel window into the evolution and architecture of the social brain, while lending new empirical support to the long-standing claim that environments shape individual notions of what is attractive.

We examined whether abnormal volumes of several brain regions as well as their mutual associations that have been observed in patients with schizophrenia, are also present in individuals at clinical high-risk (CHR) for developing psychosis. 3T magnetic resonance imaging was acquired in 19 CHR and 20 age- and handedness-matched controls. Volumes were measured for the body and temporal horns of the lateral ventricles, hippocampus and amygdala as well as total brain, cortical gray matter, white matter, and subcortical gray matter volumes. Relationships between volumes as well as correlations between volumes and cognitive and clinical measures were explored. Ratios of lateral ventricular volume to total brain volume and temporal horn volume to total brain volume were calculated. Volumetric abnormalities were lateralized to the left hemisphere. Volumes of the left temporal horn, and marginally, of the body of the left lateral ventricle were larger, while left amygdala but not hippocampal volume was significantly smaller in CHR participants compared to controls. Total brain volume was also significantly smaller and the ratio of the temporal horn/total brain volume was significantly higher in CHR than in controls. White matter volume correlated positively with higher verbal fluency score while temporal horn volume correlated positively with a greater number of perseverative errors. Together with the finding of larger temporal horns and smaller amygdala volumes in the left hemisphere, these results indicate that the ratio of lateral ventricle the ratio of temporal horns volume to brain volume is abnormal in CHR compared to controls. These abnormalities present in CHR individuals may constitute the biological basis for at least some of the CHR syndrome.

Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% female; 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD = 3.16), range 16-29). Heritability estimates for three HM components-mean translation (MT), maximum translation (MAXT) and mean rotation (MR)-ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0.76-1.00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h(2) = 42%). Using a sub-sample (N = 35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent "traits" over repeated scans (r = 0.53-0.59); reliability was even higher for the composite metric (r = 0.66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h(2) = 0.23 (sd = 0.041), BA45: h(2) = 0.26 (sd = 0.061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated.

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n = 430; aged 16–30 years), that the cerebellum is strongly, and reliably (n = 30 rescans), activated during an n-back working memory task, particularly lobules I–IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.

Schizophrenia is characterized by neuropsychological deficits across many cognitive domains. Cognitive phenotypes with high heritability and genetic overlap with schizophrenia liability can help elucidate the mechanisms leading from genes to psychopathology. We performed a meta-analysis of 170 published twin and family heritability studies of > 800 000 nonpsychiatric and schizophrenia subjects to accurately estimate heritability across many neuropsychological tests and cognitive domains. The proportion of total variance of each phenotype due to additive genetic effects (A), shared environment (C), and unshared environment and error (E), was calculated by averaging A, C, and E estimates across studies and weighting by sample size. Heritability ranged across phenotypes, likely due to differences in genetic and environmental effects, with the highest heritability for General Cognitive Ability (32%-67%), Verbal Ability (43%-72%), Visuospatial Ability (20%-80%), and Attention/Processing Speed (28%-74%), while the lowest heritability was observed for Executive Function (20%-40%). These results confirm that many cognitive phenotypes are under strong genetic influences. Heritability estimates were comparable in nonpsychiatric and schizophrenia samples, suggesting that environmental factors and illness-related moderators (eg, medication) do not substantially decrease heritability in schizophrenia samples, and that genetic studies in schizophrenia samples are informative for elucidating the genetic basis of cognitive deficits. Substantial genetic overlap between cognitive phenotypes and schizophrenia liability (average r(g) = -.58) in twin studies supports partially shared genetic etiology. It will be important to conduct comparative studies in well-powered samples to determine whether the same or different genes and genetic variants influence cognition in schizophrenia patients and the general population.

In a population-based genome-wide association (GWA) study of n-back working memory task-related brain activation, we extracted the average percent BOLD signal change (2-back minus 0-back) from 46 regions-of-interest (ROIs) in functional MRI scans from 863 healthy twins and siblings. ROIs were obtained by creating spheres around group random effects analysis local maxima, and by thresholding a voxel-based heritability map of working memory brain activation at 50%. Quality control for test-retest reliability and heritability of ROI measures yielded 20 reliable (r>0.7) and heritable (h2>20%) ROIs. For GWA analysis, the cohort was divided into a discovery (n=679) and replication (n=97) sample. No variants survived the stringent multiple-testing-corrected genome-wide significance threshold (p

NIH The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported. (c) 2016 Wolters Kluwer Health, Inc. All rights reserved. Univ Oslo, Oslo Univ Hosp, NORMENT, Oslo, Norway Harvard Univ, Massachusetts Gen Hosp, Sch Med, Ctr Human Genet Res,Dept Psychiat,Psychiat & Neur, Boston, MA USA BroInst MIT & Harvard, Stanley Ctr Psychiat Res, Cambridge, MA USA Harvard Univ, Sch Med, Brockton VA Boston Healthcare Syst, Brockton, MA 02401 USA Texas Tech Univ, Hlth Sci Ctr, Ctr Excellence Neurosci, El Paso, TX USA Virginia Inst Psychiat & Behav Genet, Richmond, VA USA Inst Psychiat Res, Neurosci Res Bldg,320 W 15th St, Indianapolis, IN 46202 USA Cardiff Univ, MRC Ctr Neuropsychiat Genet & Genom, Inst Psychol Med & Clin Neurosci, Cardiff CF10 3AX, S Glam, Wales Univ Edinburgh, Royal Edinburgh Hosp, Sch Clin Sci, Div Psychiat, Edinburgh, Midlothian, Scotland Wellcome Trust Sanger Inst, Hinxton, England UCL, Div Psychiat, London WC1E 6BT, England UCL, Div Psychiat, Mol Psychiat Lab, London WC1E 6BT, England UCL, Inst Psychiat, MRC Social Genet & Dev Psychiat Ctr, London WC1E 6BT, England Univ Hong Kong, Dept Psychiat, Hong Kong, Hong Kong, Peoples R China H Lundbeck & Co AS, Dept Synapt Transmiss, Valby, Denmark Univ Wurzburg, Dept Psychiat, Div Mol Psychiat, Wurzburg, Germany Goethe Univ Frankfurt, Dept Child & Adolescent Psychiat Psychosomat & Ps, D-60054 Frankfurt, Germany Natl Inst Mental Hlth & NeuroSci, Bangalore, Karnataka, India Univ Stellenbosch, Fac Med & Hlth Sci, SA MRC Ctr TB Res,div Mol Biol & Human Genet, DST NRF Ctr Excellence Biomed TB Res,Dept Psychia, Cape Town, South Africa Univ Helsinki, Fac Biol & Environm Sci, Dept Biosci, Helsinki, Finland Semmelweis Univ, Inst Med Chem Mol Biol & Pathobiochem, H-1085 Budapest, Hungary Univ Cagliari, Dept Biomed Sci, Sect Neurosci & Clin Pharmacol, Monserrato, Italy Univ Fed Sao Paulo, Dept Morphol & Genet, Sao Paulo, Brazil Neurosci Res Australia, Barker St Randwick, Sydney, NSW, Australia Karolinska Inst, Dept Med Epidemiol & Biostat, Solna, Sweden Radboud Univ Nijmegen, Med Ctr, Donders Inst Brain Cognit & Behav, Dept Human Genet, NL-6525 ED Nijmegen, Netherlands Univ Fed Sao Paulo, Dept Morphol & Genet, Sao Paulo, Brazil NIH: R13AA017055 Web of Science

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all meta-analyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Background: The volume of the central corpus callosum (CCC) is inversely correlated with that of bilateral lateral ventricles (LV) in first-episode schizophrenia (FESZ) (del Re, in press). Here, we determined corpus callosum (CC) diffusion measures (DTI) using 2-tensor tractography in FESZ and controls in order to investigate the relationship between diffusion in CCC and LV volume at baseline and 0.7 months later. Free-water correction was applied to account for the possible contribution of LV CSF to CC DTI measures.

The hemodynamic response function (HRF) describes the local response of brain vasculature to functional activation. Accurate HRF modeling enables the investigation of cerebral blood flow regulation and improves our ability to interpret fMRI results. Block designs have been used extensively as fMRI paradigms because detection power is maximized; however, block designs are not optimal for HRF parameter estimation. Here we assessed the utility of block design fMRI data for HRF modeling. The trueness (relative deviation), precision (relative uncertainty), and identifiability (goodness-of-fit) of different HRF models were examined and test–retest reproducibility of HRF parameter estimates was assessed using computer simulations and fMRI data from 82 healthy young adult twins acquired on two occasions 3 to 4 months apart. The effects of systematically varying attributes of the block design paradigm were also examined. In our comparison of five HRF models, the model comprising the sum of two gamma functions with six free parameters had greatest parameter accuracy and identifiability. Hemodynamic response function height and time to peak were highly reproducible between studies and width was moderately reproducible but the reproducibility of onset time was low. This study established the feasibility and test–retest reliability of estimating HRF parameters using data from block design fMRI studies.

Although key to understanding individual variation in task-related brain activation, the genetic contribution to these individual differences remains largely unknown. Here we report voxel-by-voxel genetic model fitting in a large sample of 319 healthy, young adult, human identical and fraternal twins (mean ± SD age, 23.6 ± 1.8 years) who performed an n-back working memory task during functional magnetic resonance imaging (fMRI) at a high magnetic field (4 tesla). Patterns of task-related brain response (BOLD signal difference of 2-back minus 0-back) were significantly heritable, with the highest estimates (40–65%) in the inferior, middle, and superior frontal gyri, left supplementary motor area, precentral and postcentral gyri, middle cingulate cortex, superior medial gyrus, angular gyrus, superior parietal lobule, including precuneus, and superior occipital gyri. Furthermore, high test-retest reliability for a subsample of 40 twins indicates that nongenetic variance in the fMRI brain response is largely due to unique environmental influences rather than measurement error. Individual variations in activation of the working memory network are therefore significantly influenced by genetic factors. By establishing the heritability of cognitive brain function in a large sample that affords good statistical power, and using voxel-by-voxel analyses, this study provides the necessary evidence for task-related brain activation to be considered as an endophenotype for psychiatric or neurological disorders, and represents a substantial new contribution to the field of neuroimaging genetics. These genetic brain maps should facilitate discovery of gene variants influencing cognitive brain function through genome-wide association studies, potentially opening up new avenues in the treatment of brain disorders.

Working memory-related brain activation has been widely studied, and impaired activation patterns have been reported for several psychiatric disorders. We investigated whether variation in N-back working memory brain activation is genetically influenced in 60 pairs of twins, (29 monozygotic (MZ), 31 dizygotic (DZ); mean age 24.4 ± 1.7S.D.). Task-related brain response (BOLD percent signal difference of 2 minus 0-back) was measured in three regions of interest. Although statistical power was low due to the small sample size, for middle frontal gyrus, angular gyrus, and supramarginal gyrus, the MZ correlations were, in general, approximately twice those of the DZ pairs, with non-significant heritability estimates (14–30%) in the low-moderate range. Task performance was strongly influenced by genes (57–73%) and highly correlated with cognitive ability (0.44–0.55). This study, which will be expanded over the next 3 years, provides the first support that individual variation in working memory-related brain activation is to some extent influenced by genes.