Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia

Molecular Psychiatry (2020) 25:584–602 https://doi.org/10.1038/s41380-018-0118-1 ARTICLE Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia Ida E Sønderby et al. Received: 12 February 2018 / Revised: 2 May 2018 / Accepted: 25 May 2018 / Published online: 3 October 2018 © The Author(s) 2018. This article is published with open access Abstract Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β=−0.71 to −1.37; P< 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β=−0.95, P=0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P=0.0032, 8.9 × 10−6, 1.7 × 10−9, 3.5 × 10−12 and 1.0 × 10−4, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to noncarriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes. Introduction Carriers of large recurrent copy number variants (CNVs) are at increased risk for developing autism spectrum disorders (ASD), schizophrenia or intellectual disability [1]. While the same CNV may confer risk for each of these neurodevelopmental disorders, carriers show remarkable phenotypic variability [2–5]. Magnetic resonance imaging (MRI) can help unravel possible underlying brain consequences associated with carrying these CNVs and provide novel insight into neuropathological mechanisms [2,6–10]. The neural correlates of a recurrent CNV at the distal 16p11.2 locus have remained unexplored, despite being potentially very informative. Low copy repeats at the 16p11.2 locus drive the formation of recurrent CNVs (Fig. 1)[5,11–15] whose carriers experience increased risk for various neurodevelopmental disorders [3,16–25] or somatic traits and diseases [17,26–30]. Within the 16p11.2 region, the segment with breakpoints (BP) at 28.3 and 28.9 Mb (hg18, BP1–BP3) is referred to as the distal region. Within this region, there is a minimal core segment from 28.7 to 28.9 Mb (hg18, BP2–BP3) (Fig. 1) that contains nine genes. The deletion is associated with obesity [26,27], intellectual disability [26]andschizophrenia [20,22] and the duplication has been associated with lower body mass index (BMI) [17,31]. Both 16p11.2 distal CNVs are associated with autism spectrum disorder [17]and have been found in individuals with epilepsy [23]. Several studies have been published about the microstructural effect on the brain [6,8,32] and cognition [30,33,34] of the 16p11.2 proximal CNV (29.5–30.1 MB) (Fig. 1). In contrast, the biological basis of the 16p11.2 distal phenotypes, including a possible effect on brain structure and cognition remains unknown. Large effect-size CNVs conferring risk for neurodevelopmental disorders including major psychiatric disorders [35] are rare (<0.25% in frequency). Assembling sufficiently powered MRI samples to detect effects of rare CNVs on brain morphometry is challenging. For instance, in the Icelandic population [2] and the UK Biobank [36], the frequencies of the 16p11.2 distal deletion are 0.019% and 0.012%, respectively. Likewise, the reciprocal duplication Corrected: Correction *Ole A. Andreassen [email protected] Extended author information available on the last page of the article Electronic supplementary material The online version of this article (https://doi.org/10.1038/s41380-018-0118-1) contains supplementary material, which is available to authorized users. 1234567890();,: 1234567890();,: is found at a frequency of 0.038% and 0.030%, respectively. Hence, studying rare pathogenic CNVs like 16p11.2 distal calls for collaborative efforts. The ENIGMA-CNV consortium has collected a sample that currently includes 16,046 subjects with CNV and brain MRI data. In recent studies, ‘mirrored phenotypes’were described in 16p11.2 distal CNV allele carriers for both weight [17,31] and head circumference [17]. On average, deletion carriers had increased BMI and head circumference, whereas duplication carriers had lower weight and smaller head circumference. Here we investigated gene dose response effects (i.e., effects dependent on the number of genomic copies at the 16p11.2 distal locus) on brain structural measures, including subcortical brain volumes, total surface area, mean cortical thickness and intracranial volume (ICV) in n=6906 participants from primarily nonfamilial population samples, in addition to clinical cohorts, to resolve CNV effects relative to a general population average. Material and methods Discovery sample description Supplementary Table 1 contains information on study design, participants, genotyping array, PFB-file and reference for previous description on individual inclusion and exclusion parameters for all 34 world-wide data sets in ENIGMA-CNV; altogether 16,046 individuals with CNV and MRI imaging data from the ENIGMA consortium. The 16p11.2 distal sample consisted of a subset of these individuals with twelve deletion carriers, twelve duplication carriers and 6882 non-carriers from eleven different cohorts and 14 scanner sites collected up until 1 July 2017. More demographic details are supplied in Supplementary Note 1 and Supplementary Table 2 (on CNV carriers). CNV calls and validation See Supplementary note 2 for details on CNV calling and quality control. In short, carriers in the 16p11.2 European consortium cohort were identified based on report from the cytogeneticist. All other cohorts had CNVs called in a unified manner using PennCNV [37]. Appropriate population frequency (PFB)-files (Human Genome Build NCBI36/ hg18) and GC (content)-model files for each data set were selected from the PennCNV homepage (Supplementary Table 1). Samples were filtered based on standardized quality control metrics and CNVs overlapping the region of interest (16p11.2 distal BP2–BP3 and BP1–BP3) were identified and visualized with the R package iPsychCNV. The minimally affected 16p11.2 distal region was covered well by all the arrays in the study (Supplementary Figure 1). No carriers carried other genomic imbalances (as defined by Supplementary Table 3) except six 16p11.2 distal-proximal CNV (BP1–BP5) carriers from the 16p11.2 European consortium sample (Supplementary Table 2). Image acquisition and processing Supplementary Table 4 outlines-specific technical details concerning scanners and acquisition parameters. The brain measures examined were obtained from structural MRI data Fig. 1 Recurrent CNVs in the 16p11.2 region. CNVs are indicated with reddish lines. All coordinates (in MB) are from the human genome build hg18/NCBI 36. This study includes CNVs overlapping the core 16p11.2 distal region (BP2–BP3) of 220 kb (blue box). These CNVs include the 16p11.2 distal BP2–BP3 (~220 kb), the 16p11.2 distal BP1–BP3 (~550 kb), the 16p11.2 distal BP1–BP4 (~800 kb) and the 16p11.2 distal-proximal BP1–BP5 (~1.7 MB) CNVs 16p11.2 distal copy number variant brain structure 585 collected at participating sites and processed locally following the ENIGMA protocol. The analysis was based on standardized image analysis, FreeSurfer, quality assurance and statistical methods as per the harmonized neuroimaging protocols developed within ENIGMA2 [38] and ENIGMA3 (http://enigma.ini.usc.edu/protocols/imaging-protocols). More details are supplied in Supplementary Note 3. Statistical analysis Imaging data processing and CNV calling were performed locally whereupon downstream analysis was performed centrally in a mega-analysis with de-identified data. The primary analysis for this paper focused on the full set of subjects including family members and data sets with patients to maximize the power to detect effects. Only one of a pair of duplicates was kept. Individuals with a minimum overlap of 0.4 to regions (R package iPsychCNV) with known pathogenic CNVs (Supplementary Table 3) were excluded from the analysis regardless of copy number status. Only scanner sites with individuals carrying a 16p11.2 distal deletion or duplication were included. See Supplementary Note 4 for description of control analyses excluding either (a) individuals with an established neurodevelopmental diagnosis, (b) children below age 18, (c) first and second-degree relatives or (d) carriers of the 1.7 MB 16p11.2 distal-proximal (BP1–BP5) CNV or (e) matching each carrier with four controls or (f) testing the effect of ancestry. Brain measures were normalized in R 3.2.3 by an inverse normal transformation of the residual of a linear regression on the phenotype correcting for covariates. The final covariance-corrected values (covariates =age, age squared, sex, scanner site and ICV) were used in downstream analysis and are reported for each measure. ICV was not included as a covariate in the analysis of ICV. For analytic purposes, total cortical surface area and total average thickness were normalized in the same way as subcortical volumes. We also performed analysis excluding ICV from the covariates. For the dose response analysis (i.e., the effect on brain structure of 16p11.2 distal copy number variation), a linear regression on the copy number state of the individuals (deletion =1, normal =2, duplication =3) was performed using the following model: covariance-corrected brain measure ~ copy number (deletion =1, non-carrier =2, duplication =3). For comparison between groups, a two sample two-sided t-test assuming equal variance in all carrier/non-carrier groups was employed (R 3.3.2) where deletion or duplication carriers were compared either to each other or to noncarriers. Results were considered statistically significant if they exceeded a Bonferroni-corrected P-value (P=0.05/10 regions =0.005). We report the uncorrected p-values throughout the manuscript. Effect size is calculated as the absolute effect size (the difference in mean between the two copy number groups in the t-test—which, in this case, equals Cohen’sdas the standard deviation of the normalized brain measures is one) and the estimate of beta in the linear regression. Plots were generated using R library ggplot2 v2.2.1 [39]. deCODE replication sample An independent sample of three 16p11.2 distal deletion and, six duplication carriers, as well as 832 non-carriers was obtained from deCODE Genetics, Iceland. CNVs were called with PennCNV as described previously and visually inspected. All 16p11.2 distal carriers were of the minimal 16p11.2 distal (BP2–BP3) CNV type. The individuals were scanned at one scanner site as previously described [7]. The statistical analysis was performed as for the primary discovery sample. Meta-analysis Afixed effects model was used to generate summary effect size estimates using a restricted maximum likelihood estimator in the R-package metafor-package [40] (version 1.9- 9) using the effect size and calculated SD (for comparison between groups) or standard error (for dose response) from the discovery and replication sample as input. More details can be found in Supplementary Note 5. IQ, BMI and gene expression analysis Individuals aged 18–65 years were recruited for cognitive phenotyping based on a large genotyped sample from deCODE. The Icelandic version of the Wechsler Abbreviated Scale of Intelligence (WASIIS) [41,42] was administered to 1693 non-carriers and all CNV carriers except one deletion carrier. Another 455 controls and one deletion carrier were tested with two subtests, Vocabulary and Matrix Reasoning, from the Wechsler Adult Intelligence Scale (WAIS-III) [43]. More details on tests are available in Supplementary Note 6. Carriers of known pathogenic CNVs (Supplementary Table 3) besides 16p11.2 distal as well as individuals with neurodevelopmental or psychiatric diagnoses were excluded from the analysis. IQ data were not normally distributed and, consequently, the nonparametric Kruskal–Wallis test (R, v3.2.3) was used to test differences in IQ between carrier groups. To test pairwise differences (deletion carriers versus non-carrier-con- trols, duplication carriers versus non-carrier-controls, 586 I. E. Sønderby et al. deletion versus duplication carriers), we used Wilcoxon rank test in R. We applied a significance threshold of 0.05, without correction for multiple testing since this was secondary analyses. For description of BMI and gene expression analysis, see Supplementary Note 7. Results Study participants In the ENIGMA-CNV discovery data set, we identified 12 16p11.2 distal deletion carriers and 12 duplication carriers scanned at 14 MRI scanners, and 6882 non-carriers investigated at the same MRI scanners. Demographic data are shown in Table 1. Most CNV carriers exhibited the minimal 16p11.2 distal CNV type (BP2–BP3) (Fig. 1), four CNVs were of the extended type (BP1–BP3) and six CNVs extended into the 16p11.2 proximal region (BP1–BP5) (Supplementary Table 2, Supplementary Figure 1). None of the participants carried additional known pathogenic CNVs (Supplementary Table 3). Of 24 CNV carriers, 10 had an established neurodevelopmental diagnosis (Supplementary Table 2). The remaining carriers either did not have one or were recruited in studies from which diagnostic information was not available (Supplementary Table 2, Table 1). There was a significant age difference between the groups (ANOVA, P=0.003); the non-carriers were older (mean age 43.5 years) in comparison to the deletion (27.8 years) and duplication carriers (31.2 years). In addition, an established neurodevelopmental diagnosis was found in a significantly smaller proportion of non-carriers (4.9%) in comparison to deletion (58%) and duplication carriers (25%) (Table 1). Table 1 Demographic data, discovery and replication data Discovery, ENIGMA-CNV Replication, deCODE del con dup Pdel con dup P Individuals 12 6882 12 3 832 6 Age (mean (sd)) 27.8 (20.4) 43.5 (20.0) 31.2 (19.6) 0.003 48.7 (14) 46.2 (12) 47.0 (15) NS Sex =female (%) 6 (50%) 3724 (54%) 3 (25%) NS 2 (67%) 502 (60%) 1 (17%) NS Established diagnosis, n(%) 7 (58%) 336 (4.9%) 3 (25%) <0.001 0 (0%) 158 (19%) 2 (33%) NS Diagnosis type (%)a ADHD 1 (8.3%)b2 (<0.1%) 149 (18%) 2 (33%) Autism 2 (17%) 1 (0.1%) Bipolar 1 (8.3 %) 100 (1.5 %) 7 (0.8%) Dysthymia 1 (8.3 %)c1 (<0.1%) Epilepsy 1 (8.3%) 101 (1.5%) Enuresis 1 (8.3%) Language disorder 1 (8.3%)c MDD 13 (0.2%) 1 (8.3%)d Schizophrenia 1 (8.3%) 119 (1.7%) 1 (0.1%) Scanner sites 7 14 9 1 1 1 Types of arrayse8 11 9 Various Various Various Children, n(%) 5 (41.7%) 788 (11.5%) 5 (41.7%) <0.001 0 (0) 0 (0) 0 (0) Close relative in data set, n(%) 5 (41.7%) 1994 (30.8%) 2 (16.7%) NS 3 (0%) 832 (0%) 6 (0%) Age, sex, established diagnosis, diagnosis type, number of scanner sites, types of arrays, children (number of individuals below 18 years) and individuals with close relatives (>3rd degree relatives) in the data set. P(p-value) is based on a χ2-test for categorical values and ANOVA for continuous values (R-package, TableOne) ADHD attention deficit disorder, MDD major depressive disorder, SCZ schizophrenia, del deletion carrier, con non-carriers, dup duplication carrier, NS non-significant aDiagnosis type specifies the total number within each carrier group with an established neurodevelopmental or psychiatric diagnosis bIn addition to Specific Learning disorder cIn addition to Moderate ID and Speech Sound disorder dIn addition to Social Anxiety disorder eThe arrays for ENIGMA-CNV are specified in Sup Table 1 16p11.2 distal copy number variant brain structure 587 Brain imaging results in the discovery sample After correction for age, age squared, sex and scanner site, we found a significant negative correlation between the number of 16p11.2 distal copies (deletion =1, non-carrier =2, duplication =3) and ICV (β=−0.71, P=5.1 × 10−4) (Table 2,Fig.2a) after correction for multiple testing (significance threshold P<0.005=0.05/10 brain structures analysed), showing smaller ICV in duplication carriers compared to deletion carriers. The uncorrected ICV plotted against age stratified by scanner site are shown in Fig. 2b. We evaluated whether the 16p11.2 distal CNV affected seven subcortical (accumbens, caudate, putamen, pallidum, amygdala, hippocampus and thalamus) and two cortical (total surface area and mean cortical thickness) phenotypes. After adjusting subcortical and cortical volumes for age, age squared, sex, scanner site and ICV, the volumes of caudate, pallidum and putamen were negatively associated with the number of 16p11.2 distal copies with significance at the multiple testing threshold (β=−0.87, P=2.0 × 10−5; β=−1.06, P=2.2 × 10−7and β=−1.37, P=1.8 × 10−11, respectively) (Table 2, Fig. 2a). Plotting the unadjusted volumes of caudate, pallidum and putamen against the age of participants revealed a consistent pattern (deletion carriers with larger and duplication carriers with smaller subcortical volumes in comparison to non-carriers) for all scanner sites for putamen and pallidum volume and almost at all sites for caudate (Supplementary Figure 3a-c). This shows that our findings are robust and not dependent upon a few scanner sites. To assess non-specific associations, we re-analyzed subcortical volumes without correcting for ICV. As expected, the absolute effect sizes of copy number on the volumes of caudate, pallidum and putamen increased (β=−1.18, P=6.8 × 10−9;β=−1.27, P=4.7 × 10−10 and β=−1.57, P=1.4 × 10−14, respectively) and the association with the volumes of the rest of subcortical structures, except amygdala and hippocampus, became significant (Supplementary Table 5). To test for the presence of nonlinear differences between deletion and duplication carriers, and deletion or duplication carriers and non-carriers, we conducted individual t-tests between these groups. We confirmed a negative dose response with increasing copy number for the volumes of the caudate, pallidum, putamen and for ICV but no additional structures revealed significant associations at significance threshold of <0.005 (Table 3, Supplementary Table 6, Supplementary Figure 3a-c). To confirm the validity of the results, we carefully checked for the impact of removing subjects known to carry a neurodevelopmental diagnosis, children below age 18, first and second-degree relatives or CNV carriers whose CNVs extended into the 16p11.2 proximal region (Supplementary Tables 5 and 6). Likewise, we redid the analysis matching each carrier with four non-carriers for sex, age, diagnosis status (with/without neurodevelopmental diagnosis) and scanner site and finally in a separate analysis we controlled for population stratification in cohorts with available ancestry (Supplementary Table 7). None of these analyses changed the main results. Table 2 Dose response of 16p11.2 distal copy number on subcortical volumes ENIGMA-CNV, discovery deCODE, replication Combined (discovery +replication) Brain measure βPβPQp(Q)I2 βCI, β, lower CI, β, upper P Accumbens −0.49 0.025 −0.65 0.05 0.163 0.69 0 −0.54 −0.9 −0.18 0.0032* Amygdala −0.21 0.31 −0.13 0.7 0.043 0.84 0 −0.19 −0.52 0.15 0.27 Caudate −0.87 2.0E–05** −0.46 0.17 1.13 0.29 11.4 −0.76 −1.1 −0.42 8.9E–06** Hippocampus −0.14 0.48 −0.03 0.94 0.0813 0.78 0 −0.11 −0.45 0.22 0.52 Pallidum −1.06 2.2E–07** −0.95 0.0042* 0.0813 0.78 0 −1.03 −1.37 −0.7 1.7E–09** Putamen −1.37 1.8E–11** −0.7 0.034 3.01 0.083 66.8 −1.19 −1.53 −0.85 3.5E–12** Thalamus −0.33 0.11 −0.37 0.27 0.0105 0.92 0 −0.34 −0.69 0.01 0.054 Surface Area −0.09 0.66 0.14 0.68 0.355 0.55 0 −0.03 −0.36 0.31 0.87 Thickness −0.29 0.16 −0.02 0.94 0.49 0.48 0 −0.22 −0.55 0.12 0.2 ICV −0.71 5.1E–04* −0.54 0.1 0.194 0.66 0 −0.66 −1−0.33 1.0E–04** The effect size (βof the linear regression) is presented. A linear regression based on the copy number state of the individuals (deletion =1, noncarrier =2, duplication =3) was performed on normalized brain measures correcting for age [2], age, sex and scannersite (and ICV) in the ENIGMA-CNV (discovery) and deCODE (replication) cohorts. Results were considered statistically significant if they were below a Bonferronicorrected P-value of 0.005 (0.05/10 regions). A final effect size estimate of the combined sample was obtained using a fixed effects meta-analysis framework CI confidence interval, Qstatistics for the test for heterogeneity, p(Q) p-value for the test for heterogeneity, I2 heterogeneity levels *P< 0.005 **P< 0.0005 588 I. E. Sønderby et al. Replication in an independent cohort We performed replication of the subcortical findings in an Icelandic MRI sample (deCODE) comprising 841 individuals (3 deletion and 6 duplication carriers, 832 non-car- riers) (Table 1). The negative correlation between the number of 16p11.2 distal copies and the volume of pallidum was confirmed (β=−0.95, P=0.0042) (Fig. 3, Table 2)at a significance threshold of <0.005. For volumes of the caudate, putamen and for ICV, effects were in the same direction as in the discovery sample, albeit not significant (β=−0.46, P=0.17; β=-0.70, P=0.034; β=−0.54, P=0.10, respectively) (Fig. 3, Table 2). Apart from cortical surface area, we observed the same direction of effect in the replication sample as in the discovery sample (Table 2, Fig. 3). For nonlinear differences, all directions of effect were the same for subcortical volumes in the discovery and replication data sets (Table 3, Supplementary Figure 4a-c). The combined analysis of the ENIGMA and the deCODE samples is shown in Table 2and Fig. 3. Volumes of caudate, pallidum, putamen and ICV decreased with increasing number of 16p11.2 distal copies (β=−0.76, P=8.9 × 10−6;β=−1,03, P=1.7 × 10−9;β=−1.19, P=3.5 × 10−12;β=−0.66, P=1.0 × 10−4, respectively). In the combined analysis, the volume of the accumbens also revealed a significant association with the 16p11.2 distal copy number (β=−0.54, P=0.0032) (Tables 2and 3, Fig. 3, Supplementary Figure 4a-c). Fig. 2 Measures of caudate, pallidum, putamen and ICV show a dose response to differences in copy number in the 16p11.2 distal region. All analyses were corrected for age, age squared, sex, scanner site and ICV (except for ICV). Deletion carriers (del) in red, non-carriers (con) in grey and duplication carriers (dup) in blue, respectively. aBoxplots of subcortical volumes, surface area and thickness and ICV. The normalized brain values are presented. Boxplots represent the mean. Significant differences after Bonferroni correction between groups are noted as *P< 0.005, **P< 0.0005. Centre line represents median, box limits are the upper and lower 25% quartiles, whiskers the 1.5 interquartile range and the points are the outliers. bBivariate plot of age versus uncorrected ICV 16p11.2 distal copy number variant brain structure 589 Intelligence quotient (IQ) Full scale IQ data were available for four 16p11.2 distal deletion and twelve duplication carriers and 2148 noncarriers from the Icelandic sample. None of these individuals had an established neurodevelopmental diagnosis, or other known pathogenic CNVs (as defined by Supplementary Table 3). Analysis showed a significant difference in IQ between groups (P=0.0042). Both deletion (median IQ = 68.5) and duplication carriers (median IQ =93) presented a significantly lower IQ (P=0.011, P=0.035) than noncarriers (median IQ =101.5) (Supplementary Table 9, Supplementary Figure 5) at a significance threshold of P< 0.05. Body mass index (BMI) BMI data for mega-analysis were available for six cohorts from ENIGMA-CNV counting seven deletion and seven duplication carriers in addition to 1880 individuals without a 16p11.2 distal CNV (Supplementary Table 9). BMI zscores were different between the carrier groups (Kruskal–Wallis, P=0.009, Supplementary Figure 6, Supplementary Table 10). Duplication carriers had Table 3 T-test on subcortical volumes between different 16p11.2 distal copy number groups ENIGMA-CNV, discovery deCODE, replication Combined, discovery +replication Brain measure Cohens DP Cohens DP n, CN1 n, CN2 Qp(Q) I2 Cohens DCI, lower CI, upper P Deletion vs duplication Accumbens −0.99 0.023 −1.86 0.044 13 17 0.99 0.32 0 −1.17 −1.87 −0.47 1.0E–03* Amygdala −0.42 0.32 −0.27 0.73 15 18 0.031 0.86 0 −0.38 −1.09 0.32 0.28 Caudate −1.74 0.0013* −1.25 0.25 15 18 0.251 0.62 0 −1.63 −2.44 −0.82 8.0E–05** Hippocampus −0.29 0.57 −0.16 0.79 15 18 0.034 0.85 0 −0.22 −0.91 0.47 0.53 Pallidum −2.11 1.8E–05** −2.19 0.0038* 15 18 0.0091 0.92 0 −2.13 −2.79 −1.47 3.2E–10** Putamen −2.73 5.3E–06** −1.67 0.089 15 18 0.77 0.38 0 −2.58 −3.41 −1.74 1.4E–09** Thalamus −0.66 0.18 −0.85 0.045 15 17 0.079 0.78 0 −0.76 −1.41 −0.1 0.024 Surface area −0.18 0.69 −0.04 0.96 15 18 0.012 0.91 0 −0.16 −0.98 0.66 0.7 Thickness −0.58 0.25 0.17 0.84 15 18 0.53 0.47 0 −0.41 −1.26 0.45 0.35 ICV −1.41 0.0084 −0.70 0.4 15 18 0.65 0.42 0 −1.19 −1.99 −0.4 0.0034* Deletion vs non-carrier-controls Accumbens −0.54 0.088 −1.78 0.0019* 13 2467 3.1 0.078 67.8 −0.73 −1.22 −0.23 0.0039* Amygdala 0.08 0.79 −0.15 0.8 15 3578 0.11 0.74 0 0.04 −0.49 0.56 0.89 Caudate −0.63 0.027 −1.12 0.05 15 3574 0.46 0.5 0 −0.79 −1.45 −0.12 0.02 Hippocampus −0.26 0.37 −0.23 0.69 15 3578 0.0031 0.96 0 −0.24 −0.75 0.27 0.35 Pallidum −0.82 0.0043* −1.52 0.0081 15 3577 0.87 0.35 0 −0.95 −1.51 −0.38 1.0E–03* Putamen −1.72 2.4E–09** −1.24 0.031 15 3584 0.18 0.67 0 −1.67 −2.35 −0.99 1.5E–06** Thalamus −0.3 0.3 −0.59 0.3 15 3571 0.24 0.62 0 −0.42 −1 0.16 0.15 Surface area 0.24 0.4 −0.50 0.38 15 4312 0.41 0.52 0 0.14 −0.61 0.9 0.71 Thickness −0.28 0.32 0.43 0.45 15 4312 0.66 0.42 0 −0.13 −0.83 0.58 0.73 ICV −0.44 0.13 0.23 0.7 15 4314 0.97 0.33 0 −0.26 −0.85 0.32 0.38 Non-carrier-controls vs duplication Accumbens −0.34 0.26 −0.08 0.84 4433 17 0.25 0.62 0 −0.27 −0.73 0.19 0.26 Amygdala −0.46 0.11 −0.13 0.76 4533 18 0.41 0.52 0 −0.36 −0.83 0.11 0.13 Caudate −1.04 3.0E−04** −0.12 0.77 4617 18 1.9 0.17 46.2 −0.92 −1.37 −0.47 6.1E−05** Hippocampus 0.06 0.85 0.08 0.84 4557 18 0.0030 0.96 0 0.07 −0.35 0.49 0.75 Pallidum −1.1 0.00013** −0.66 0.1 4504 18 2.3 0.13 56.5 −0.86 −1.14 −0.58 1.9E−09** Putamen −0.85 0.0031* −0.44 0.28 4454 18 0.99 0.32 0 −0.7 −1.09 −0.31 4.9E−04** Thalamus −0.33 0.27 −0.25 0.54 4562 17 0.063 0.8 0 −0.27 −0.5 −0.03 0.025 Surface area −0.33 0.25 0.46 0.26 4575 18 3.0 0.086 66.1 −0.19 −0.53 0.16 0.28 Thickness −0.28 0.33 −0.24 0.55 4578 18 0.0054 0.94 0 −0.27 −0.75 0.21 0.28 ICV −0.88 0.0023* −0.93 0.022 4690 18 0.0068 0.93 0 −0.9 −1.45 −0.35 0.0014* Deletions versus duplication carriers, deletion carriers versus non-carriers and non-carriers versus duplication carriers in ENIGMA (discovery), deCODE (replication) and the combined sample. T-tests were performed on normalized values of brain measures correcting for age [2], age, sex and scanner site (and ICV). Results were considered significant if they were below a Bonferroni-corrected P-value of 0.005 (0.05/10 regions). A final Cohen’sd-effect size estimate of the combined sample was obtained using a fixed effects meta-analysis framework CI confidence interval, Qstatistics for the test for heterogeneity, p(Q) p-value for the test for heterogeneity, I2 heterogeneity levels *P< 0.005 **P< 0.0005 590 I. E. Sønderby et al. significantly lower BMI z-scores (median; SD =−0.65; 1.61) than the non-carriers (0.43; 1.19; P=0.048). Also, the duplication carriers tended to have lower BMI z-scores than the deletions (−1.56; 1.1, P=0.052; Supplementary Figure 6, Supplementary Table 10) and deletion carriers tended to have higher BMI z-scores than non-carriers (P= 0.18) (Supplementary Table 10). For detailed information on individual carriers, see Supplementary Table 2. Discussion This is the first study to determine the brain structure underpinnings of the 16p11.2 distal recurrent CNVs. We found a common denominator for 16p11.2 distal carriers across different clinical phenotypes in a dose response effect of copy number on the volumes of basal ganglia (caudate, putamen and pallidum) (Table 2, Figs. 2and 3). The observed associations were independent of the presence of neurodevelopmental diagnosis and the ancestry of participants (Supplementary Tables 5 and 7). These effects were consistent in the independent replication sample (Fig. 3, Tables 2and 3). Together with the result of lower IQ in carriers, these findings provide new insight into genetic mechanisms of brain structures and pathobiological processes involved in neurodevelopmental disorders. There are nine genes in the core 16p11.2 distal (BP2–BP3) region. We tested the expression level of these Fig. 3 Forest plots on the dose response of copy number on subcortical volumes, surface area, thickness and ICV. The effect size (βof the linear regression) at each site for each measure is shown by the position on the x-axis. Standard error is shown by the horizontal line. A summary polygon shows the results when fitting a random-effects model to the two groups: ENIGMA-CNV discovery and deCODE replication samples. del, con and dup denote the number of individuals in each analysis. *P< 0.005, **P< 0.0005. Effect size and confidence intervals are to the right 16p11.2 distal copy number variant brain structure 591 in blood in available transcript data from two of our deletion carriers (BP1–BP3) and compared with 234 non-carriers (Supplementary Figure 7, Supplementary Table 11). Several transcripts of the nine genes were relatively decreased in blood. Due to the low numbers, only trends can be suggested from these data but we find the down-regulation of LAT gene expression to ~65% in 16p11.2 distal carriers (Supplementary Figure 7, Supplementary Table 11) particular interesting due to recent results in zebrafish: These showed that of the nine genes in 16p11.2 distal, only overexpression of LAT induced a decrease in cell proliferation in the brain with a concomitant microcephaly phenotype [44]. In parallel, LAT knockout mice showed brain anatomy changes [44]. According to the Allen Brain Atlas, LAT shows the highest expression in cerebellum and structures of the basal ganglia (data not shown). Thus, high expression of LAT overlaps with the position of brain structural changes identified in the present study. This further implicates LAT, an immune signaling adaptor, as a possible dosagedependent driver of the CNV-associated brain phenotypes including the basal ganglia. By comparing the effect of a range of CNVs on the brain, it is possible to identify patterns of effects related to the genes involved, and thus learn about biological mechanisms. All three CNVs previously shown to have an effect on ICV, 16p11.2 proximal [6,8], 22q11 [9] and Williams Syndrome [45], have concomitantly identified an effect on either cortical surface area and/or cortical thickness. We observed no effect on cortical surface area or cortical thickness in 16p11.2 distal carriers (Table 2, Figs. 2and 3). This does not rule out the presence of smaller effects on individual cortical areas. However, it may suggest a different impact on brain development mechanisms between these three CNVs [6,8,9,45] and 16p11.2 distal. CNVs in the two neighboring regions 16p11.2 distal and 16p11.2 proximal show overlapping phenotypes: they both dispose to various neurodevelopmental diseases and both show a positive dose response for head circumference and weight [17]. In addition, we found negative dose response effects for the 16p11.2 distal CNV on ICV, putamen and caudate volumes with effect size estimates comparable to those previously reported for the 16p11.2 proximal CNV [8]. Recently, a lymphoblastoid cell line study of chromosomal interaction in the 16p11.2 region suggested that the two adjacent 16p11.2 distal and 16p11.2 proximal regions (Fig. 1) interact [17]. The identified brain commonalities could further support a mechanism in which the similar phenotypic patterns are caused by disruption of the chromatin structure surrounding the entire 16p11.2 region [17,32]. In this study, the CNVs (3 deletions and 3 duplications) of six carriers extend into the adjacent 16p11.2 proximal region. Redoing the analysis with exclusively 16p11.2 distal carriers did not result in a change in effect size (Supplementary Tables 5 +6), suggesting that 1.7 MB distalproximal (BP1–BP5) CNV carriers are not the main cause of the signal. A previous analysis from Loviglio et al. [44] suggests an additive effect of two 16p11.2 regions (distal + proximal) on human head circumference and weight. Together with our data, this indicates both separate and overlapping effects for the two CNVs and underlines the importance of studying specific CNVs independently despite overlapping phenotypes. Interestingly, deletions and duplications of both 16p11.2 proximal and distal CNVs are associated with ASD [17,19]. However, only the proximal duplication and the distal deletion are associated with schizophrenia [20,22]. This difference in phenotype association between these bordering CNVs may indicate specific differences in the pathological mechanisms of ASD and schizophrenia. We observed a decrease in absolute effect sizes for putamen and pallidum after removing individuals with a neurodevelopmental diagnosis (Supplementary Table 5). This is consistent with the enlargements of putamen and pallidum associated with duration of illness in schizophrenia [46], which may partly reflect the cumulative effect of antipsychotic medication on basal ganglia volumes [47]. The observed dose response effect on ICV is in agreement with previous findings on head circumference [17], as are the dose response effect on BMI (Supplementary Figure 6, Supplementary Table 10) [17,31]. One of the strengths of this study is the inclusion of non-clinical samples allowing for estimates closer to the actual carrier population. Unfortunately, the small number of CNV cases does not provide enough power to investigate preferential alterations in deletion or duplication carriers. To conclude, the present findings of negative doseresponse effects of copy number on ICV and volumes of caudate, pallidum and putamen, with no effect on cortical measures, suggest a specific effect on basal ganglia structures of the 16p11.2 distal CNV. These results provide novel insight into genetic factors determining basal ganglia volumes and suggest specific pathobiological mechanisms involved in the development of neurodevelopmental disorders. Acknowledgements 1000BRAINS: 1000BRAINS is a populationbased cohort based on the Heinz-Nixdorf Recall Study and is supported in part by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The HNR study is also supported by the German Ministry of Education and Science (FKZ 01EG940), and the German Research Council (DFG, ER 155/6-1). The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 7202070 (Human Brain Project SGA1; Katrin Amunts, Sven Cichon). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network 592 I. E. Sønderby et al. 16p11.2 copy number variants on brain structure: a multi-site ‘genetic-first’study. Biol Psychiatry. 2018; https://doi.org/10. 1016/j.biopsych.2018.02.1176. 33. Hippolyte L, Maillard AM, Rodriguez-Herreros B, Pain A, Martin-Brevet S, Ferrari C, et al. The number of genomic copies at the 16p11.2 locus modulates language, verbal memory, and inhibition. Biol Psychiatry. 2016;80:129–39. 34. Hanson E, Bernier R, Porche K, Jackson FI, Goin-Kochel RP, Snyder LG, et al. The cognitive and behavioral phenotype of the 16p11.2 deletion in a clinically ascertained population. Biol Psychiatry. 2015;77:785–93. 35. Insel TR. Rethinking schizophrenia. Nature. 2010;468:187–93. 36. Kendall KM, Rees E, Escott-Price V, Einon M, Thomas R, Hewitt J, et al. 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Eur Arch Psychiatry Clin Neurosci. 2016;266:451–60. Affiliations Ida E Sønderby1●Ómar Gústafsson2●Nhat Trung Doan1●Derrek P Hibar3,4 ●Sandra Martin-Brevet5● Abdel Abdellaoui6,7 ●David Ames8,9 ●Katrin Amunts10,11,12 ●Michael Andersson13 ●Nicola J Armstrong14 ● Manon Bernard15 ●Nicholas Blackburn16 ●John Blangero16 ●Dorret I Boomsma17 ●Janita Bralten18 ● Hans-Richard Brattbak19,20 ●Henry Brodaty21 ●Rachel M Brouwer22 ●Robin Bülow23 ●Vince Calhoun24 ● Svenja Caspers10,11,12 ●Gianpiero Cavalleri25 ●Chi-Hua Chen26,27 ●Sven Cichon28,29,30 ●Simone Ciufolini31 ● Aiden Corvin32 ●Benedicto Crespo-Facorro33,34 ●Joanne E Curran16 ●Anders M Dale27 ●Shareefa Dalvie35 ● Paola Dazzan36,37 ●Eco J C de Geus38,39 ●Greig I. de Zubicaray40 ●Sonja M. C. de Zwarte22 ●Norman Delanty25,41 ● Anouk den Braber38,42 ●Sylvane Desrivières43 ●Gary Donohoe44,45 ●Bogdan Draganski46,47 ●Stefan Ehrlich48,49,50 ● Thomas Espeseth1,51 ●Simon E Fisher52,53 ●Barbara Franke18,53,54 ●Vincent Frouin55 ●Masaki Fukunaga56 ● Thomas Gareau55 ●David C Glahn57,58 ●Hans Grabe59 ●Nynke A. Groenewold35 ●Jan Haavik60 ●Asta Håberg61 ● Ryota Hashimoto62 ●Jayne Y Hehir-Kwa63 ●Andreas Heinz64 ●Manon H. J. Hillegers22,65 ●Per Hoffmann29,30,66 ● Laurena Holleran67 ●Jouke-Jan Hottenga6●Hilleke E Hulshoff22 ●Masashi Ikeda68 ●Neda Jahanshad3● Terry Jernigan69 ●Christiane Jockwitz10,12,70 ●Stefan Johansson20,60 ●Gudrun A Jonsdottir2●Erik G Jönsson1,71 ● Rene Kahn22 ●Tobias Kaufmann1●Sinead Kelly67 ●Masataka Kikuchi72 ●Emma E M Knowles73 ●Knut K Kolskår1,51,74 ● John B Kwok75 ●Stephanie Le Hellard76,77 ●Costin Leu78,79 ●Jingyu Liu80,81 ●Astri J Lundervold60,82 ● Arvid Lundervold83 ●Nicholas G. Martin84 ●Karen Mather85 ●Samuel R. Mathias73 ●Mark McCormack86,87 ● Katie L. McMahon88 ●Allan McRae89 ●Yuri Milaneschi90 ●Clara Moreau91 ●Derek Morris44,45 ●David Mothersill67 ● Thomas W Mühleisen28,29 ●Robin Murray92 ●Jan E Nordvik74 ●Lars Nyberg13 ●Loes M Olde Loohuis93 ● Roel Ophoff22,93 ●Tomas Paus94,95,96,97 ●Zdenka Pausova15 ●Brenda Penninx98 ●Juan M Peralta16 ●Bruce Pike99 ● Carlos Prieto100 ●Sara Pudas13,101 ●Erin Quinlan102 ●Daniel S Quintana1,51 ●Céline S Reinbold29,30 ● Tiago Reis Marques36,103 ●Alexandre Reymond104 ●Genevieve Richard1,51,74 ●Borja Rodriguez-Herreros5,91 ● Roberto Roiz-Santiañez33,34 ●Jarek Rokicki1●James Rucker37,43 ●Perminder Sachdev85 ●Anne-Marthe Sanders1,51,75 ● Sigrid B Sando61,105 ●Lianne Schmaal106,107,108 ●Peter R Schofield109,110 ●Andrew J. Schork27 ●Gunter Schumann43 ● Jean Shin15,93 ●Elena Shumskaya18,53 ●Sanjay Sisodiya111,112 ●Vidar M Steen76,77 ●Dan J Stein113,114 ● Stacy Steinberg2●Lachlan Strike115 ●Alexander Teumer116 ●Anbu Thalamuthu85 ●Diana Tordesillas-Gutierrez34,117 ● Jessica Turner118 ●Torill Ueland1●Anne Uhlmann35,119,120 ●Magnus O. Ulfarsson2,121 ●Dennis van ’t Ent6● Dennis van der Meer1●Neeltje E. M. van Haren22 ●Anja Vaskinn1●Evangelos Vassos122 ●G. Bragi Walters2,123 ● Yunpeng Wang1●Wei Wen85 ●Christopher D Whelan86 ●Katharina Wittfeld124 ●Margie Wright84,125 ● 16p11.2 distal copy number variant brain structure 599 Hidenaga Yamamori126 ●Tetyana Zayats60,83 ●Ingrid Agartz1●Lars T Westlye 1,51 ●Sébastien Jacquemont91,127 ● Srdjan Djurovic76,128 ●Hreinn Stefánsson2●Kári Stefánsson2,123 ●Paul Thompson3●Ole A. Andreassen 1●for the 16p11.2 European Consortium, for the ENIGMA-CNV working group 1NORMENT, K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway 2deCODE Genetics/Amgen, Reykjavik, Iceland 3Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, USA 4Janssen Research and Development, La Jolla, CA, USA 5Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland 6Biological Psychology, Vrije Universiteit Amsterdam, van Boechorststraat 1, 1081 BT Amsterdam, The Netherlands 7Department of Psychiatry, Academic Medical Center, Amsterdam, The Netherlands 8National Ageing Research Institute, Melbourne, Australia 9Academic Unit for Psychiatry of Old Age, University of Melbourne, Melbourne, Australia 10 Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Wilhelm-Johnen-Str., 52425 Juelich, Germany 11 C. and O. Vogt Institute for Brain Research, Medical Faculty, University of Dusseldorf, Merowingerplatz 1A, 40225 Dusseldorf, Germany 12 JARA-BRAIN, Juelich-Aachen Research Alliance, Wilhelm- Johnen-Str., 52425 Juelich, Germany 13 Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187 Umeå, Sweden 14 Mathematics and Statistics, Murdoch University, Perth, Australia 15 The Hospital for Sick Children, University of Toronto, Toronto M5G 1X8, Canada 16 South Texas Diabetes and Obesity Institute, Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, One West University Blvd., 78520 Brownsville, TX, USA 17 Netherlands Twin Register, Vrije Universiteit, van der Boechorststraat 1, 1081BT Amsterdam, Netherlands 18 Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands 19 Department of Clinical Science, University of Bergen, Bergen, Norway 20 Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway 21 Centre for Healthy Brain Ageing and Dementia Collaborative Research Centre, UNSW, Sydney, Australia 22 Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands 23 Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany 24 The Mind Research Network, The University of New Mexico, Albuquerque, NM, Mexico 25 The Royal College of Surgeons in Ireland, 123 St Stephen’s Green, Dublin 2, Ireland 26 Department of Radiology, University of California San Diego, La Jolla, USA 27 Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, USA 28 Institute of Neuroscience and Medicine (INM-1), Structural and Functional Organisation of the Brain, Genomic Imaging, Research Centre Juelich, Leo-Brandt-Strasse 5, 52425 Jülich, Germany 29 Human Genomics Research Group, Department of Biomedicine, University of Basel, Hebelstrasse 20, 4031 Basel, Switzerland 30 Institute of Medical Genetics and Pathology, University Hospital Basel, Schönbeinstrasse 40, 4031 Basel, Switzerland 31 Psychosis Studies, Insitute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespingy Park, SE5 8AF London, United Kingdom 32 Neuropsychiatric Genetics Research Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin 2, Ireland 33 Department of Medicine and Psychiatry, University Hospital Marques de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, 39008 Santander, Spain 34 CIBERSAM (Centro Investigacion Biomedica en Red Salud Mental), Santander 39011, Spain 35 Department of Psychiatry and Mental Health, Anzio Road, 7925 Cape Town, South Africa 36 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, SE5 8AF London, United Kingdom 37 National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom 38 Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit, van der Boechorststraat 1, 1081 BT Amsterdam, Netherlands 39 Amsterdam Neuroscience, VU University medical center, van der Boechorststraat 1, 1081 BT Amsterdam, NH, Netherlands 40 Faculty of Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia 41 Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology and Center for Neuroscience, University of 600 I. E. Sønderby et al. California at Davis, 4860 Y Street, Suite 3700, Sacramento, California 95817, USA 42 Alzheimer Center and Department of Neurology, VU University Medical Center, De Boelelaan 1105, 1081HV Amsterdam, Netherlands 43 Medical Research Council –Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom 44 Cognitive Genetics and Cognitive Therapy Group, Neuroimaging, Cognition & Genomics Centre (NICOG) & NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland 45 Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Psychiatry, Trinity College Dublin, Dublin 8, Ireland 46 LREN - Département des neurosciences cliniques, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland 47 Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany 48 Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, 01307 Dresden, Germany 49 Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, USA 50 Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA 51 Department of Psychology, University of Oslo, Oslo, Norway 52 Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, Netherlands 53 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands 54 Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands 55 NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur- Yvette, France 56 Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi, Japan 57 Yale University School of Medicine, 40 Temple Street, Suite 6E, 6511 New Haven, Vaud, USA 58 Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, 300 George Street, 6106 Hartford, CT, USA 59 Department of Psychiatry und Psychotherapy, University Medicine Greifswald, Greifswald, Germany 60 K.G. Jebsen Centre for Neuropsychiatric Disorders, University of Bergen, Bergen, Norway 61 Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway 62 Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University, Suita, Osaka, Japan 63 Princess Máxima Center for Pediatric Oncology, Lundlaan 6, 3584 EA Utrecht, The Netherlands 64 Dept. of Psychiatry and Psychotherapie, Charite, Humboldt University, Chariteplatz 1, 10017 Berlin, Germany 65 Child and adolescent Psychiatry / Psychology, Erasmus medical center-Sophia’s Childerens hospitaal, Rotterdam, Wytemaweg 8, 3000 CB Rotterdam, The Netherlands 66 Institute of Human Genetics, University of Bonn, Sigmund-Freud- Str. 25, 53127 Bonn, Germany 67 The Centre for Neuroimaging & Cognitive Genomics (NICOG) and NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland 68 Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan 69 Center for Human Development, University of California San Diego, San Diego, CA, USA 70 Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Medical Faculty, Pauwelsstraße 30, 52074 Aachen, Germany 71 Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Karolinska University Hospital Solna, R5:00, SE-17176 Stockholm, Sweden 72 Department of Genome Informatics, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka 565-0871, Japan 73 Department of Psychiatry, Yale University, 40 Temple Street, 6515 New Haven, CT, USA 74 Sunnaas Rehabilitation Hospital HT, Nesodden, Norway 75 Brain and Mind Centre, University of Sydney, Sydney, Australia 76 NORMENT - KG Jebsen Centre, Department of Clinical Science, University of Bergen, Jonas Lies veg 87, 5021 Bergen, Norway 77 Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Jonas Lies veg 87, 5021 Bergen, Norway 78 Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA 79 Institute of Neurology, University College London, London, United Kingdom 80 The Mind Research Network, 1101 Yale Blvd., 87106 Albuquerque, CT, USA 81 Dept. of Electrical and Computer Engineering, University of New Mexico, 87131 Albuquerque, New Mexico, USA 82 Department of Biological and Medical Psychology, Jonas Lies vei 91, N-5009 Bergen, Norway 83 Department of Biomedicine, University of Bergen, 5009 Bergen, Norway 84 QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia 16p11.2 distal copy number variant brain structure 601 85 Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, New South Wales 2052, Australia 86 Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, 123 St. Stephens Green, D02 YN77 Dublin, Ireland 87 Centre for Molecular Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands 88 Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia 89 Program in Complex Trait Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia, Queensland, Australia 90 Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands, Oldenaller 1, 1081 HJ Amsterdam, The Netherlands 91 CHU Sainte-Justine Research Center, Université de Montréal, Montréal, QC, Canada 92 Departments of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom 93 Center for Neurobehavioral Genetics, University of California, Los Angeles, California 90095, USA 94 Rotman Research Institute, University of Toronto, Toronto M6A 2E1, Canada 95 Department of Psychiatry, University of Toronto, Toronto M5S 1A1, Canada 96 Center for Developing Brain, Child Mind Institute, New York, NY 10022, USA 97 Department of Psychology, University of Toronto, Toronto M5S 1A1, Canada 98 Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, VU University Medical, Amsterdam, Netherlands 99 Departments of Radiology & Clinical Neuroscience, University of Calgary, Calgary T2N 1N4, Canada 100 Bioinformatics Service, Nucleus, University of Salamanca (USAL), 37007 Salamanca, Spain 101 Department of Integrative Medical Biology, Linnéus väg 9, 901 87 Umeå, Sweden 102 Centre for Population Neuroscience and Stratified Medicine, Social, Genetic and Development Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, SE5 8AF London, UK 103 Psychiatry Imaging Group, MRC London Institute of Medical Sciences, Faculty of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, W12 0NN London, UK 104 Center for Integrative Genomics, University of Lausanne, Genopode building, CH-1015 Lausanne, Switzerland 105 Department of Neurology, University Hospital of Trondheim, Edvard Griegs gate 8, N-7006 Trondheim, Norway 106 Orygen, The National Centre of Excellence in Youth Mental Health, 35 Poplar Road, 3502 Parkville, New Mexico, Australia 107 Centre for Youth Mental Health, The University of Melbourne, 35 Poplar Road, 3502 Parkville, Victoria, Australia 108 Department of Psychiatry, VU University Medical Center, 1007 MB Amsterdam, The Netherlands 109 Neuroscience Research Australia, Randwick, Australia 110 School of Medical Sciences, University of New South Wales, Sydney, Australia 111 Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK 112 Chalfont Centre for Epilepsy, London, UK 113 Dept of Psychiatry, University of Cape Town, Groote Schuur Hospital, Anzio Rd, 7925 Cape Town, South Africa 114 MRC Unit on Risk & Resilience in Mental Disorders, Stellenbosch, South Africa 115 Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia 116 Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany 117 Neuroimaging Unit, Technological Facilities. Valdecilla Biomedical Research Institute IDIVAL, Santander, Cantabria 39011, Spain 118 Department of Psychology, Georgia State University, Atlanta, GA, USA 119 Department of Psychiatry, Stellenbosch University, TBH Francie van Zijl Avenue, 7500 Cape Town, South Africa 120 Department of Psychiatry, 1 South Prospect Street, 5401 Burlington, Vermont, USA 121 Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland 122 MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, SE5 8AF London, UK 123 Faculty of Medicine, University of Iceland, Reykjavik, Iceland 124 German Center for Neurodegenerative Diseases (DZNE), Rostock, Greifswald, Greifswald, Germany 125 Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia 126 Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka, Japan 127 Department of Pediatrics, University of Montreal, Montreal H3C 3J7, Canada 128 Department of Medical Genetics, Oslo University Hospital, Kirkeveien 166, 424 Oslo, Norway 602 I. E. Sønderby et al.