Clara A Moreau
email: clara.moreau@pasteur.fr

Kuldeep Kumar

Annabelle Harvey

Guillaume Huguet

Sebastian Urchs

Elise A Douard

Laura M Schultz

Hanad Sharmarke

Khadije Jizi

Charles-Olivier Martin

Charles- Olivier Martin

Nadine Younis

Petra Tamer

Thomas Rolland

Jean-Louis Martineau

Pierre Orban

David Shin

Ana Isabel Silva

Jeremy Hall

Marianne B M Van Den Bree

Michael J Owen

David E J Linden

Aurelie Labbe

Anne M Maillard

Tomasz J Nowakowski

Sarah Lippé

Carrie E Bearden

Laura Almasy

David C Glahn

Paul M Thompson

Thomas Bourgeron

Pierre Bellec

Sebastien Jacquemont
email: sebastien.jacquemont@umontreal.ca

Atlas of functional connectivity relationships across rare and common genetic variants, traits, and psychiatric conditions

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Introduction

Genetic pleiotropy and polygenicity are key features of psychiatric conditions [START_REF] Lee | Pleiotropy and Cross-Disorder Genetics Among Psychiatric Disorders[END_REF]. Common and rare variants both contribute to risk. Genetic correlation (rG) is a measure of genetic overlap between conditions, also referred to as pleiotropy. It is moderate to high between schizophrenia (SZ), bipolar disorder (BIP), and major depressive disorder (MDD) and milder between these three conditions and autism spectrum disorder (ASD) (2)[START_REF] Grove | Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium, BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium[END_REF][START_REF] Pardiñas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF]. Varying levels of genetic correlations are also observed between conditions, brain morphometry, cognitive and behavioral traits (5,[START_REF] Sniekers | Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence[END_REF]. Although overlap has mostly been computed for common variants (single nucleotide polymorphisms, SNPs), data shows that pleiotropy also applies to rare variants such as copy number variants (CNVs) [START_REF] Chawner | Genotype-phenotype associations in children with copy number variants associated with high neuropsychiatric risk in the UK (IMAGINE-ID): a case-control cohort study[END_REF][START_REF] Douard | Effect Sizes of Deletions and Duplications on Autism Risk Across the Genome[END_REF], which are associated with a broad range of cognitive phenotypes and overlapping psychiatric diagnoses.

Polygenicity is particularly problematic for studying risk and mechanisms. The latter has been modeled for SNPs, using polygenic scores (PGS), which are weighted sums of genetic risk alleles [START_REF] Zheutlin | Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems[END_REF]. Similar models applied to rare variants show that the effects of CNVs on cognition are the sum of individual effects of genes encompassed in the CNV, weighted by their sensitivity to gene dosage [START_REF] Huguet | Measuring and Estimating the Effect Sizes of Copy Number Variants on General Intelligence in Community-Based Samples[END_REF][START_REF] Huguet | Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability[END_REF].

An optimistic interpretation of polygenicity is that genomic variants may converge on a smaller set of mechanisms -amenable to intervention-at some level from gene transcription to microcircuits to large-scale connectivity networks to behavior [START_REF] Gandal | Polygenicity in Psychiatry-Like It or Not, We Have to Understand It[END_REF]. A less optimistic interpretation embraces complexity. In this scenario, convergence would occur very late -or not at all-in the 'pathway' to behavior. Polygenicity would therefore lead to "polyconnectivity"; that is, each genomic variant leads to a mostly distinct large-scale brain connectivity profile (Figure 1A) [START_REF] Flint | Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease[END_REF]. In the latter scenario, individuals with the same diagnosis would present a broad array of distinct alterations. It is unknown whether polygenicity leads to shared or distinct alterations of brain connectivity pathways implicated in psychiatric conditions.

Understanding the effects of polygenicity and pleiotropy on brain architecture may therefore help to identify and target broad psychiatric risk pathways amenable to common therapeutic interventions [START_REF] Gandal | Polygenicity in Psychiatry-Like It or Not, We Have to Understand It[END_REF].

The organization of large-scale functional networks in the brain can be inferred using restingstate functional MRI (rs-fMRI). This imaging technique measures spontaneous, lowfrequency temporal synchronization of the activity in different brain regions during rest [START_REF] Biswal | Functional connectivity in the motor cortex of resting human brain using echo-planar MRI[END_REF][START_REF] Van Den Heuvel | Exploring the brain network: a review on resting-state fMRI functional connectivity[END_REF]. Several studies demonstrated that functional networks are related to the spatial distribution of gene expression in the brain, especially genes linked to ion channel activity and synaptic function [START_REF] Richiardi | Correlated gene expression supports synchronous activity in brain networks[END_REF][START_REF] Hawrylycz | Canonical genetic signatures of the adult human brain[END_REF]. Functional connectivity (FC) has gained traction, characterizing increasingly reproducible patterns of FC alterations associated with psychiatric conditions [START_REF] Holiga | Patients with autism spectrum disorders display reproducible functional connectivity alterations[END_REF]. Studies have provided critical insight into the architecture of brain networks involved in neuropsychiatric disorders and have demonstrated overlap at the connectivity level (rFC) between conditions [START_REF] Moreau | Dissecting autism and schizophrenia through neuroimaging genomics[END_REF] It is unknown whether brain FC overlap between conditions reflects genetic pleiotropy.

Knowledge gap:

How polygenicity and pleiotropy reflect aspects of large-scale brain functional networks in neuropsychiatric disorders is unknown.

Genetics-first studies investigate individuals selected on the basis of specific genomic variants irrespective of psychiatric symptoms or diagnoses. Such approaches offer opportunities to investigate effects of genetic factors on FC irrespective of clinical diagnoses. Deletions and duplications at the 16p11.2 and 22q11.2 loci have been associated with mirror effects on 6 global FC (i.e., average connectivity strength across all regions in the brain) [START_REF] Moreau | Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia[END_REF]. In contrast to diagnostic-first studies, effect-sizes for high-risk neuropsychiatric mutations were of similar magnitude for neuroimaging features and behavioral traits [START_REF] Moreau | Dissecting autism and schizophrenia through neuroimaging genomics[END_REF]. Connectivity profiles of the thalamus, somatomotor, posterior insula and cingulate showed substantial similarities between neuropsychiatric CNVs and idiopathic ASD and SZ, but not attention deficit hyperactivity disorder (ADHD). Beyond these two genomic loci, nothing is known about the effects of rare variants on network connectivity. As well, little is known about the FC effects of PGS (additive common genomic risk) for psychiatric conditions [START_REF] Cao | Functional connectome-wide associations of schizophrenia polygenic risk[END_REF]. Overall, the relationship between FC and neuropsychiatric variants is understudied.

Our overarching aim was to investigate brain connectivity relationships between multiple genetic risks, traits, and diseases to provide insight into mechanisms underlying polygenicity and pleiotropy in psychiatric conditions.

Specifically, we aimed to: 1) Characterize the FC profiles of rare neuropsychiatric CNVs ranging from mono-or oligo-genic CNVs (i.e., involving a single gene or small gene sets) to large polygenic CNVs (eg. n=50 genes for 22q11.2 deletion) and PGS; 2) Investigate the relationship between the level of polygenicity and effects of genomic variants on FC; 3) Investigate the relationship of previously established genetic correlations (referred as pleiotropy) to FC similarities between conditions and traits; 4) Identify brain networks associated with genetic risks, psychiatric diseases, and traits.

To this end, we processed all rs-fMRI data using the same pipeline in n=32,988 individuals.

We performed 36 connectome-wide association studies (CWAS, Figure 1B) on 1) 1,003 carriers of one among 7 neuropsychiatric and 9 non-psychiatric CNVs identified from 4 clinical cohorts and the UK Biobank; 2) 30,185 and 174 non-CNV carriers from the UK Biobank and clinical cohorts respectively, 3) 778 individuals with idiopathic ASD, SZ, BIP or ADHD from 4 datasets and their respective 848 controls (Table 1). The following data have never been published: 876 CNV carriers, all PGS, all brain morphometry traits, and two out of the four genetics-first cohorts.

Results

Effect sizes of 36 FC profiles across genetic risk, conditions, cognitive and brain morphometry traits

We computed brain-wide FC profiles with and without global signal adjustment (GSA; Figures 2A-E, Supplementary table 4). FC-profiles were defined as the 2,080 β values of 2,080 connections -connectivity between 64 functional parcels-obtained from the contrast of cases vs. controls. All connectivity values were z-scored, based on the variance of corresponding control groups. Compared to controls, six CNVs, ASD, SZ, and all of the brain and cognitive traits showed a mild shift in mean FC (mean of all beta values; Table 2, Figure 2A-E). After GSA, FC profiles remained mutually correlated. It was previously demonstrated that GSA-FC profiles show stronger correlations with cognition [START_REF] Li | Global signal regression strengthens association between resting-state functional connectivity and behavior[END_REF], and reduce confounding effects in multi-site studies [START_REF] Yan | Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes[END_REF]. We, therefore, used GSA-FC profiles for the remainder of this study.

Effect-sizes were largest for neuropsychiatric CNVs followed by psychiatric conditions, brain morphometry, cognitive traits, and PGS. All 7 neuropsychiatric and one out of nine "nonpsychiatric" CNVs altered connections that survived false discovery rate (FDR) (2,080 connections, q<0.05, Table 2). Sensitivity analyses -performing contrasts in 5,000 randomly sampled groups -found the same level of significance compared to FDR procedure (Table 2). The 16p11.2 deletion and duplication showed large effects on FC-profiles, which were . correlated (r=0.7 and 0.83) to the previously published profiles that were based on smaller samples. The previously published 22q11.2 deletion FC profile also showed large effects.

1q21.1 deletion and duplication FC profiles showed moderate to large effects on FC. 15q11.2 deletion and duplications showed the mildest effects among the neuropsychiatric CNVs.

None of the brain-related PGS computed in the UKBB showed mean shifts in global connectivity, but all of them (except cortical thickness and ASD) altered GSA-FC profiles with 5 to 116 connections surviving FDR. The non-brain PGS traits showed no significant effects surviving FDR and the permutation test (Table 1, Figure 2). Effect sizes were on average one order of magnitude smaller than those observed for CNVs. The correlation between the FC profiles of PGS and their corresponding conditions and traits (rFC) ranged from 0.23 to 0.73 (p=0.01 to 0.0001). This suggests that connectivity alterations observed in psychiatric conditions are also observed, to some extent, in healthy individuals at increased risk.

Patients diagnosed with idiopathic SZ, BIP, ASD but not ADHD significantly altered FC compared to controls. For SZ, ADHD, and ASD, profiles were previously published (20) but we recomputed them for ADHD and SZ after adding individuals. Correlations between new and previously published profiles were 0.70 and 0.95, respectively.

All cognitive and brain morphometry traits were associated with FC profiles of mild effectsizes. Sensitivity analyses showed that results were robust to the effects of sex, pooled or matched controls, clinical or non-clinical ascertainment, and medication (Supplemental results).

Increasing polygenicity decreases functional connectivity signals

Previous studies demonstrated that the effect-size of CNVs on IQ increases approximately linearly with the number of encompassed genes. The number of deleted genes (weighted by dosage sensitivity) predicts IQ with 78% accuracy [START_REF] Huguet | Measuring and Estimating the Effect Sizes of Copy Number Variants on General Intelligence in Community-Based Samples[END_REF][START_REF] Huguet | Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability[END_REF]. We observed a correlation between the effect size of CNVs on FC and their previously estimated effect size on cognitive ability(10) (r=0.86, p=2.06e -05 ) and risk for either ASD [START_REF] Moreno-De-Luca | Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts[END_REF][START_REF] Sanders | Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci[END_REF] or SZ (26,[START_REF] Kirov | The penetrance of copy number variations for schizophrenia and developmental delay[END_REF] (r=0.76, p=0.02). These relationships appeared non-linear (Figure 2F, G), suggesting that effect size on FC may not be a mere additive effect of individual genes encompassed in CNVs.

Accordingly, the effect size of deletions and duplications normalized by the number of genes they encompass significantly decreased from a single gene to large polygenic CNVs (Figure 2H). This relationship was linear on a log-log scale (r=-0.85, p=3.0e -05 ). In other words, large multigenic CNVs have much smaller effects on FC than expected based on the number of genes they encompass (additive model). This same decrease in effect-size was observed when accounting for the level and number of dosage-sensitive genes within CNVs (r= -0.88, p= 6.9e -06 ).

In line with this observation, effect sizes of PGS for ASD, SZ, and IQ (common variants) were approximately 6-fold smaller (Figure 2F-G) than those observed for CNVs with similar effect sizes on cognition and risk for ASD and SZ.

Pleiotropy: Brain connectivity mirrors genetic correlations between conditions and traits.

Genetic overlap (measured by SNP-based genetic correlations: rG) is a key feature of the architecture of psychiatric conditions, brain morphometry, and cognitive and behavioral traits (2,5,[START_REF] Sniekers | Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence[END_REF]. A similar overlap between conditions has also been shown at the gene expression level (transcriptomic correlations: rT,, Figure 3A) [START_REF] Gandal | Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap[END_REF]. We asked if FC correlations (rFC) between conditions and traits show similar relationships. Specifically, genetic pleiotropy and 10 polygenicity may imply that genes converge on shared FC alterations leading to an increasing overlap (correlation values) from genes to transcriptome to connectome (Figure 1AB).

We first demonstrated a significant concordance between rT and rFC across 10 pairs of conditions and traits (CCC=0.76, 95%CI: [0.41; 0.91] without any bias (bias correction factor = 0.99; Figure 3B)).

We also showed a significant concordance between rG and rFC across 24 pairs of conditions and traits (CCC=0.71, 95%CI: [0.45; 0.86], bias correction factor = 0.99, Figure 3C). In other words, FC overlap between conditions and traits was neither higher nor lower than rG (Figure 1A). In a sensitivity analysis, we replaced FC profiles of traits and conditions with the FC profiles of their corresponding PGS. This resulted in the same concordance (CCC=0.71, 95%CI: [0.54;0.83], bias correction factor = 0.97). Finally, adding 3 brain morphometry traits (n=38 pairs) did not change the level of concordance (Figure 3D).

Most networks are affected by genetic risk and conditions.

All functional brain networks were affected by neuropsychiatric CNVs, PGS, psychiatric conditions, and traits (Figure 4). Basal ganglia-thalamus and somatomotor networks showed over-connectivity across most genetic risk measures and conditions. In contrast, ventral attention and auditory/posterior insula networks were predominantly under-connected. Dysconnectivity in the default mode and limbic networks exhibited the largest effect-sizes (Figure 4).

A landscape of functional correlation across genetic risk, psychiatric conditions, and traits

We further investigated the correlations between FC profiles presented above. This analysis was limited to the 20 whole-brain FC-profiles with significantly altered connections (FDR, 5AE). Although, for the most part, genetic risk, diseases, and traits presented singular FC profiles (rFC < 0.5), the correlation matrix revealed a primary cluster of shared connectivity between ASD, SZ, BIP, 16p11.2, 1q21.1, and 22q11.1 deletions and duplications, 15q11.2 deletion, as well as neuroticism, PGS-MDD, PGS-ASD, and PGS-SZ. This "neuropsychiatric cluster" was negatively correlated with cluster 2 ('cognition cluster') driven by G-factor, Fluid intelligence, PGS-IQ, CT, SA, and PGS-SA.

Thalamo-sensorimotor alterations are shared across CNVs, PGS, and idiopathic conditions.

To characterize networks underlying clusters, we performed a principal component analysis (PCA) across the 14 FC-profiles encompassed in the neuropsychiatric cluster.

Two dimensions explained respectively 26% and 10% of the variance of the FC profiles. Dimension 1 was dominated by increased connectivity involving the somatomotor and thalamus-basal ganglia networks (Figure 5B). Dimension 2 was characterized by decreased connectivity between the visual network, the posterior-medial DMN, and the ventral attention and salience networks (Figure 5C). Because neuroticism and psychiatric conditions showed higher loadings on dimension 1 than CNVs (Figure 5D), we also performed a second PCA on CNVs separately, demonstrating that similar networks and connections were contributing to the main dimension (r=0.73).

The regional FC profiles of the thalamus and dorsolateral motor network showed, as expected, similar clusters with much higher similarities among genetic risk, conditions, and traits (24 and 60 out of 190 correlations survived FDR respectively) (Figure 6A and Supplemental Figure 4).

Thalamic overconnectivity is spatially associated with glutamatergic thalamic neurons.

12 Finally, we sought to identify transcriptomic or cellular correlates of the thalamic overconnectivity observed across genetic risk and conditions. Towards this goal, we took advantage of gene co-expression relationships defined in a brain-wide laser-capture microdissection-microarray gene expression analysis conducted by the Allen Institute for Brain Science (AHBA) [START_REF] Hawrylycz | Canonical genetic signatures of the adult human brain[END_REF]. Eigengenes of co-expression modules were correlated with the 20 thalamic FC-profiles. The 'neuropsychiatric' cluster was significantly correlated with transcriptional modules involving the thalamus (ventral, dorsal, and thalamocortical), striatum, and neocortex, but negatively correlated with modules involving cerebellar cortex (M17, M24) (Figure 6B).

To further refine our analysis, we sought to predict which cell types could represent key drivers of positive correlation between AHBA transcriptomic modules and thalamic FC profiles. We took advantage of a multi-brain region single nucleus RNA sequencing dataset generated for the normal adult human cerebral cortex [START_REF] Lake | Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain[END_REF], which provides key cell type specific marker genes for glutamatergic (excitatory), GABAergic (inhibitory), and nonneuronal cells. Thalamic FC profiles of the neuropsychiatric cluster were significantly associated with marker genes of 4 glutamatergic cell types, oligodendrocytes, and somatostatin interneurons. In contrast, genes enriched in GABAergic neurons and astrocytes were negatively correlated with psychiatric FC-profiles. Together, these mapping efforts suggest that glutamatergic thalamic neurons strongly correlate with thalamic FC profiles shared across multiple conditions, and foreshadow future analyses incorporating brain-wide single-cell molecular signatures identified using cellular-resolution biochemical studies.

Discussion

Main findings

Using the largest connectome-wide characterization of rare and common genetic risk, idiopathic psychiatric conditions, and traits, we showed that:

1) Common and rare genetic risk factors for psychiatric conditions impacted most FC networks when sample sizes provided sufficient power. Effect-sizes of CNVs on FC were correlated and consistent with their effects on cognitive ability and risk for ASD-SZ;

2) Polygenicity had a profound impact on FC signals: As CNVs increased in size and number of deleted or duplicated genes, their effect size on FC did not increase in an additive fashion and rapidly tapered off. Accordingly, PGS have minute effects on FC compared to CNVs, including those with similar effect-sizes on IQ and risk for disease;

3) The level of overlap between conditions and traits was stable from genes to transcription to whole-brain FC.

4) Overlaps at the whole-brain FC level were mild to moderate across genetic risk, conditions, and traits. Functional alterations driving these similarities included overconnectivity of the thalamus and somatomotor networks. Thalamic FC profiles of conditions and genetic risk were spatially associated with marker genes of excitatory thalamic neurons and thalamic transcriptional modules.

Even small levels of polygenicity increase heterogeneity at the functional connectivity level

Our results suggest that polygenicity has a profound impact on FC signals. Although CNVs encompassing more dosage-sensitive genes have a larger effect-size on FC, this effect appears non-linear and tapers off. The effect size normalized for the number of genes or dosagesensitive genes declines rapidly for increasingly multigenic CNVs (by an order of magnitude).

. In contrast, effect-sizes on IQ is a linear (additive) function of the number of dosage-sensitive genes encompassed in CNVs (i.e., a dosage-sensitive gene decreases IQ by about 3 pointsregardless of the size and number of genes encompassed in deletions or duplications). Multigenic CNVs may therefore represent heterogeneous combinations of relatively distinct FC profiles associated with each dosage-sensitive gene. This suggests that genes within a CNV or a polygenic score may cancel out each other's effects on FC, leading to weaker effectsizes. This is striking for PGS, which show much smaller effects on FC (roughly 6-fold) than those observed for CNVs even after matching for their effects on intelligence or risk for SZ and ASD. PGS will therefore require functional partitioning in order to observe the expected effects on brain traits. Polygenicity may therefore predominantly result in "polyconnectivity", a scenario where thousands of ASD or SZ genomic risk variants lead to a diverse set of connectivity patterns associated with the conditions.

Shared FC profiles across conditions and traits parallel genetic and transcriptomic overlap.

Our results demonstrate a stable overlap between conditions and traits at the genomic, transcription, and large-scale connectivity levels. This suggests that a major component of FC-profile correlations reflects genetically-based biological processes (Figure 1A), consistent with the heritability of functional networks [START_REF] Elliott | Genome-wide association studies of brain imaging phenotypes in UK Biobank[END_REF][START_REF] Anderson | Heritability of individualized cortical network topography[END_REF]. Similar concordance was previously reported for genetic and transcriptomic [START_REF] Gandal | Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap[END_REF][START_REF] Radonjić | Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders[END_REF], as well as genetic and cortical thickness, overlaps [START_REF] Patel | Writing Committee for the Attention-Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, Bipolar Disorder, Major Depressive Disorder, Obsessive-Compulsive Disorder, and Schizophrenia ENIGMA Working Groups[END_REF]. Stable concordance from molecular to large-scale brain networks is less likely to indicate a strong mechanistic convergence at any level from micro-to macroscopic measures. In other words, distinct genomic variants may lead to largely distinct FC profiles.

Genetic risks converge on the thalamus and somatomotor network.

Although whole-brain FC profiles were largely specific with mild correlations (r<0.5), genetic risk, psychiatric conditions, and neuroticism formed an FC cluster driven by shared overconnectivity of the thalamus / basal ganglia and the somatomotor networks. The implication of the somatomotor and basal ganglia/thalamus network across genetic risk and psychiatric conditions is in line with previous transdiagnostic and single condition neuroimaging studies [START_REF] Kebets | Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology[END_REF][START_REF] Van Dam | Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels[END_REF].

These functional hubs may be highly sensitive to a broad range of genetic risks for neuropsychiatric conditions. This may be related to the fact that functional and structural measures of the thalamus, basal ganglia [START_REF] Roshchupkin | Heritability of the shape of subcortical brain structures in the general population[END_REF], and unimodal regions (ie somatomotor) show less interindividual variability and higher heritability compared to heteromodal regions [START_REF] Anderson | Heritability of individualized cortical network topography[END_REF].

G-factor and fluid intelligence showed the opposite thalamic pattern. This is in line with prior functional MRI studies demonstrating that thalamocortical pathways are engaged in memory, attention, and mental representations [START_REF] Hwang | The Human Thalamus Is an Integrative Hub for Functional Brain Networks[END_REF][START_REF] Wolff | The Cognitive Thalamus as a Gateway to Mental Representations[END_REF].

Thalamic FC profiles were spatially correlated with transcription modules implicating the thalamus, and genes highly expressed in excitatory neurons. The thalamus is composed of diverse inhibitory and excitatory neuronal types distributed across anatomically defined nuclei. Thalamocortical projection neurons are largely excitatory [START_REF] Halassa | Thalamocortical Circuit Motifs: A General Framework[END_REF], and therefore our spatial correlation is consistent with a hypothesis that changes in the activity of these cells are the primary driver of the thalamic FC alterations identified in our study. We anticipate that future studies incorporating single-cell transcriptomic profiles from the thalamus and other subcortical structures in the human brain will further refine these hypotheses.

Limitations

Small sample sizes did not allow us to accurately characterize connectivity alterations for several CNVs. However, when samples were sufficiently powered, FC profiles appear to be robust -as shown by their correlation with previously published results.

This multisite study including clinically and non-clinically ascertained cohorts may have introduced biases. Confounding factors include sex bias, age differences, and medication status, which may have influenced some of the results. However, carefully conducted sensitivity analyses, matching control groups for sex, site, age, motion, and excluding individuals with medications (in idiopathic psychiatric cohorts) provided similar results (Supplemental results).

Unknown confounders could introduce a correlation structure between FC profiles that matches SNP-level genetic correlations, but "non-brain" related conditions and traits used in our analyses as controls suggest that this is unlikely.

Conclusion

This systematic rsfMRI investigation of genetic risks and conditions has important implications for the identification of large-scale brain mechanisms involved in schizophrenia and autism.

Currently, polygenic scores are too heterogeneous for meaningful neuroimaging studies. On the other hand, rare genomic disorders are associated with an extremely diverse landscape of brain profiles. Future studies will require both in-depth partitionings of polygenic scores as

Materials and Methods

Sample

We analyzed 32,988 individuals from nine datasets (Table 1, Figure 1B, Supplementary Materials and Methods). CNVs were also identified in an unselected population (UK Biobank) (see Supplementary Materials and Methods for the CNV calling procedure and final sample description).

The 9 non-psychiatric CNVs (light grey font) were defined as variants without any previous association with psychiatric conditions in large cases control studies [START_REF] Moreno-De-Luca | Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts[END_REF][START_REF] Sanders | Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci[END_REF](26)[START_REF] Jønch | 15q11.2 Working Group, Estimating the effect size of the 15Q11.2 BP1-BP2 deletion and its contribution to neurodevelopmental symptoms: recommendations for practice[END_REF].

Idiopathic psychiatric conditions and respective controls

Individuals with idiopathic ASD, SZ, ADHD, BIP and their respective controls were sampled from 4 multicenter datasets [START_REF] Poldrack | A phenome-wide examination of neural and cognitive function[END_REF][START_REF] Martino | The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism[END_REF][START_REF]The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience[END_REF][START_REF] Orban | Altered brain connectivity in patients with schizophrenia is consistent across cognitive contexts[END_REF] (Supplementary Materials and Methods).

. Individuals with idiopathic ASD and their respective controls were sampled from the ABIDE1 multicenter dataset [START_REF] Martino | The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism[END_REF]. Individuals with idiopathic SZ and their respective controls were obtained from aggregated fMRI data of 10 studies, and from the UCLA Consortium for Neuropsychiatric Phenomics (CNP) [START_REF] Poldrack | A phenome-wide examination of neural and cognitive function[END_REF]. Individuals diagnosed with ADHD (DSM-IV) and their respective controls were obtained from the ADHD-200 dataset [START_REF]The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience[END_REF] and the CNP.

Individuals with idiopathic BIP and their respective controls were also sampled from the CNP dataset (Supplementary Materials and Methods).

Each cohort analyzed in this study was approved by the research ethics review boards of the respective institutions. Signed informed consent was obtained from all participants or their legal guardians before participation. Secondary analyses of the listed datasets for the purpose of this project were approved by the research ethics review board at Sainte Justine Hospital.

Imaging data were acquired with site-specific MRI sequences.

Resting-state functional MRI Preprocessing and QC procedures

All datasets were preprocessed using the same parameters with the same Neuroimaging Analysis Kit (NIAK), an Octave-based open-source processing and analysis pipeline [START_REF] Bellec | Proceedings of the 17th International Conference on Functional Mapping of the Human Brain[END_REF].

Preprocessed data were visually controlled for quality of the co-registration, head motion, and related artefacts by three raters (Supplementary Materials and Methods).

Computing connectomes

We segmented the brain into 64 functional seed-based regions defined by the multi-resolution MIST brain parcellation [START_REF] Urchs | MIST: A multi-resolution parcellation of functional brain networks[END_REF]. FC was computed as the temporal pairwise Pearson's correlation between the average time series of the 64 seed-based regions, and then Fisher-z transformed. The connectome of each individual encompassed 2,080 connectivity values: (63x64)/2 = 2016 region-to-region connectivity + 64 within seed-based region connectivity.

We chose the 64 parcel atlas of the multi-resolution MIST parcellation as it falls within the range of network resolution previously identified to be maximally sensitive to FC alterations in neurodevelopmental disorders such as ASD [START_REF] Abraham | Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example[END_REF]. We corrected for multiple comparisons using a false discovery rate strategy [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF].

Statistical analyses were performed in Python using the scikit-learn library [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF]. Analyses were visualized in Python and R. Code for all analyses and visualizations is available online through the GitHub platform with Jupyter notebook: https://github.com/claramoreau9/NeuropsychiatricCNVs_Connectivity

Polygenic Scores via Bayesian regression and continuous shrinkage priors (PRS-CS)

PRS-CS (51) was used to infer posterior effects using only those SNPs for individuals of European ancestry (EUR) from the UKBB dataset that were also present in both the discovery GWAS summary statistics and an external 1000 Genomes EUR-ancestry linkage disequilibrium (LD) panel. Posterior effects were computed using discovery GWAS for MDD [START_REF] Howard | Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions[END_REF], SZ [START_REF]Electronic address: douglas.ruderfer@vanderbilt.edu, Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes[END_REF], ASD (3), total cortical SA,(5) average CT,(5) inflammatory bowel disease (IBD),(54) Low-Density Lipoprotein (LDL), (55) and Chronic Kidney Disease (CKD) (56) (Supplementary method). Note that the discovery GWAS used for IQ, MDD, and CKD included individuals from the UKBB. To ensure convergence of the underlying Gibbs sampler algorithm, we ran 25,000 Markov chain Monte Carlo iterations and designated the first 10,000 MCMC iterations as burn-in. The PRS-CS global shrinkage parameter was set to 0.01 when the discovery GWAS had a sample size that was less than 200,000; otherwise, it was learned from the data using a fully Bayesian approach. Default settings were used for all other PRS-CS parameters. The EUR posterior effects were fed into PLINK 1.9 (57) to produce raw PGS separately for the EUR and white British UKBB cohorts, and R (58) was used to standardize the PGS for each cohort to mean = 0 and SD = 1. Standardized PGS were then adjusted by regressing out the first ten within-ancestry PCs.

Statistical analyses

Connectome-wide association studies (CWAS) CWAS was conducted by linear regression at the connectome level, in which z-scored FC was the dependent variable and clinical status or continuous trait was the explanatory variable.

Controls were included for all sites which included cases. FC was standardized (z-scored) based on the variance of the controls used for each CWAS. Models were adjusted for sex, scanning site, head motion, global signal (='GSA'), and age. We determined whether a connection was significantly altered by the clinical status effect by testing whether the β value (regression coefficient associated with the clinical status variable) was significantly different from 0 using a two-tailed t-test. This regression test was applied independently to each of the 2,080 functional connections. We corrected for the number of tests (2,080) using the Benjamini-Hochberg correction for FDR at a threshold of q < 0.05 [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF], following the previously published recommendations(59). Cohen's d values can therefore be interpreted as z-scores.

Before GSA, we defined the effect size of global FC shift as the mean of the β values across all 2,080 connections.

After GSA, the mean of β values was equal to zero and overall FC effect-size of a genetic variant or condition was defined as the top decile of all 2080 β values.

We tested if the effect sizes were significantly different from zero by conducting a permutation test, shuffling the clinical status labels of the individuals included in each CWAS (using 5,000 permutations). We thus estimated a valid permutation-based p-value associated with the observed global FC shift and the observed FC effect size [START_REF] Phipson | Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn[END_REF].

We performed 36 CWAS (Figure 1B):

. 1) comparing FC between cases and controls for 16 CNVs at the 15q11.2, 1q21.1, 2q13, 16p13.11, 13q12.12, 17p12, 16p11.2, 22q11.2, TAR-1q21.1, and 15q13.3 loci, as well as for 4 idiopathic psychiatric cohorts (ASD, SZ, BIP, and ADHD). FC was standardized (z-scored) based on the variance of the respective control group.

2) investigating the linear effect of 10 continuous polygenic scores: ASD, MDD, SZ, Cross-disorder, Surface Area, Cortical Thickness as well as three non-brain related control traits IBD, LDL, and CKD.

3) investigating the linear effect of 6 continuous traits provided by UK-Biobank:

Freesurfer derivatives (SA, CT, and volume), Neuroticism, and cognitive scores (G-Factor, Fluid intelligence).

All continuous traits were normalized within the UKBB sample.

Concordance between functional, genetic, and transcriptomic correlation

We computed correlations of whole-brain connectome profiles across pairs of conditions and traits (Pearson correlation) using the 2,080 beta values of each CWAS.

We obtained genetic correlation (rG) values across pairs of conditions and traits (neuroticism [START_REF] Nagel | Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways[END_REF], intelligence (6), cross-disorder (2), brain morphological traits (5)) from previously published GWAS and the database of genetic correlations across traits (ldsc.broadinstitute.org). We also obtained correlation values of transcriptomic profiles between 10 pairs of conditions [START_REF] Gandal | Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap[END_REF].

We performed concordance analyses between correlation at the genetic (rG), and functional connectivity (rFC) levels as well as the transcriptomic (rT) and the FC (rFC) levels using DescTools R package [START_REF] Team | R: A Language and Environment for Statistical Computing (Version 3.5. 2, R Foundation for Statistical Computing[END_REF]. The bias correction factor quantifies how far the best fit line deviates from 45 degrees.

Atlas of functional connectivity correlations across genetic risk, traits, and conditions.

We computed Pearson correlations between the 20 out of 36 whole-brain FC-profiles with significantly altered connections (FDR-corrected). For the significance of correlations between FC profiles, we generated a null distribution of 10,000 correlation values for each pair of conditions and traits. These 10000 null correlations were computed using null FC profiles. The latter were obtained by conducting 5,000 CWAS after shuffling the clinical status or trait values.

To obtain a p-value, the correlation value was compared to the null distribution. We corrected for the number of correlations (n=190) using the Benjamini-Hochberg correction for FDR at a threshold of q < 0.05 [START_REF] Benjamini | Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing[END_REF].

Cluster extraction and Principal Component analysis

We defined 2 clusters using hierarchical clustering (hclust function from stats R package) on the 20 FC-profiles. To identify the FC networks driving the 'neuropsychiatric' cluster 1, we conducted a PCA on the 14 scaled FC-Profiles within cluster 1 using the prcomp function from stats R package. Functional connections with top decile loadings for principal components 1 and 2 (PC1, PC2) were represented on chord diagrams using the circlize R package (code available on Github).

Aligning gene expression maps and functional parcellation

To investigate the transcriptomic relationship of FC-profiles, we aligned gene expression values in the adult human brain from the AHBA dataset [START_REF] Hawrylycz | Canonical genetic signatures of the adult human brain[END_REF] to the MIST64 brain parcellation [START_REF] Urchs | MIST: A multi-resolution parcellation of functional brain networks[END_REF], as described in [START_REF] Moreau | Mutations associated with neuropsychiatric conditions delineate functional brain connectivity dimensions contributing to autism and schizophrenia[END_REF][START_REF] Urchs | MIST: A multi-resolution parcellation of functional brain networks[END_REF][START_REF] Iūtė | A practical guide to linking brain-wide gene expression and neuroimaging data[END_REF][START_REF] Markello | abagen: A toolbox for the Allen Brain Atlas genetics data[END_REF].

AHBA co-expression modules and eigengenes

We tested the association between gene co-expression modules and FC-profiles by computing spatial correlation (Pearson r) between expression patterns of eigengenes of co-expression modules and FC-profiles. We used eigengenes for the 18 major AHBA co-expression modules [START_REF] Hawrylycz | Canonical genetic signatures of the adult human brain[END_REF]. The significance of the spatial correlation was tested using label-shuffling (permutation test by performing similar contrasts in 5000 randomly sampled groups) and BrainSMASH [START_REF] Burt | Generative modeling of brain maps with spatial autocorrelation[END_REF]. K-means clustering was used for grouping co-expression modules and FC-profiles (ComplexHeatmap package in R, consensus k-means clusters with 1000 iterations are reported).

Cell-Type classes and marker genes

We assessed the association between cell-type classes and FC-profiles by computing spatial correlation (Pearson r) between gene expression patterns of cell-type marker genes and FCprofiles. To be consistent with previous studies of rs-fMRI and AHBA gene expression data, we used marker genes for 16 transcriptionally defined cell classes previously used in MDD [START_REF] Anderson | Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder[END_REF] and SZ investigations (66). The cell marker genes are defined based on differential expression in each cell type relative to others, using Cortical single-nucleus droplet-based sequencing (snDrop-seq) data from [START_REF] Lake | Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain[END_REF] Bellec is a fellow ("Chercheur boursier Junior 2") of the "Fonds de recherche du Québec -Santé", Data preprocessing and analyses were supported in part by the Courtois foundation (Dr Bellec). This work was supported by Simons Foundation Grant Nos. SFARI219193 and SFARI274424. We thank all of the families at the participating Simons Variation in Individuals Project (VIP) sites, as well as the Simons VIP Consortium. We appreciate obtaining access to imaging and phenotypic data on SFARI Base. Approved researchers can 36

Figures and Tables

Legend: (A-E) Density plots represent the distribution of the 2,080 beta values of each FC-profile obtained by connectome-wide association study (CWAS) for (A) idiopathic condition, (B) CNVs, (C-D) traits, (E) Polygenic scores (PGS). X-axis values of all density plots represent beta values, which

were obtained from linear models computed using z-scored connectomes based on the variance of the control group. Left column of density plots shows effects on global FC signal without GSA and stars represent significant shifts of mean connectivity. Right column of density plots shows effect sizes after

GSA and stars represent FC-profiles with at least one altered connection surviving FDR. (F)

Correlation between previously published effect-sizes of CNVs on IQ [START_REF] Huguet | Measuring and Estimating the Effect Sizes of Copy Number Variants on General Intelligence in Community-Based Samples[END_REF] and their effect-sizes on FC.

The X-axis is the mean decrease in IQ associated with each CNV. (G) Correlation between previously published effect-sizes of CNVs on ASD-SZ risk (24-27) and their effect-sizes on FC. The X-axis for is the Odd ratio of each CNV computed in previously published case-control studies. PGS are not included in the correlation. (H) Correlation between CNV gene content (X-axis) and effect-sizes on FC normalized by the number of genes encompassed in CNVs. Y-axes of all figures (F-H) are effect sizes on FC after GSA (the top decile of beta-values).

Abbreviations .

Figure .3 Concordance across genetic, transcriptomic and connectomic correlations. Legend: A) Concordance between genetic correlation and transcriptomic correlation across pairs of conditions and traits (as previously published(28) with updated genetic correlations) b) The same concordance analysis was performed between FC correlations and transcriptomic correlations using the same conditions and traits. c) Concordance between genetic correlation and FC correlations across pairs of conditions and cognitive-behavioral traits. d) The same concordance as is (c) with

brain morphometry traits.

X and Y axis: r values of correlations. The brain correlations (rFC) represent the correlation between

the FC profiles of a pair of conditions-traits. The diagonal represents a perfect concordance.

Transcriptomic correlations [START_REF] Gandal | Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap[END_REF] and genetic correlation values were previously published (Supplementary Table 5). All the correlations values are available in Supplemental Table 5.

.

38

Colors indicate papers that computed rG : ( purple (2), green [START_REF] Lee | Cross-Disorder Group of the Psychiatric Genomics Consortium[END_REF], orange [START_REF] Sniekers | Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence[END_REF], blue [START_REF] Nagel | Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways[END_REF], red ( 5)). Legend: Brain maps represent max beta value per seed-region for each FC-profile with connections surviving FDR (Table 2). Red = overconnectivity ; blue underconnectivity. color scale represents the beta value (z-score).

Since we could not compute the FC profile of MDD and IBD

The Sankey plot (middle of the Figure) shows dysconnectivity across 12 networks for genetic risk (left), conditions, and traits (right). The thickness of the connecting lines represents effect sizes (mean beta value of all FDR connections between a network and the rest of the brain). The length of rectangles

represents the sum of effect sizes, which penalizes groups with higher statistical power.

Abbreviations: DEL=Deletion, DUP=Duplication, ASD: autism spectrum disorder; SZ: schizophrenia; BIP: Bipolar disorder; PGS: Polygenic score; MDD: major depressive disorder;

CrossD: Cross-Disorder.

.

B. Brain maps represent thalamic FC profiles (64 beta values for each connection between the thalamus and all other functional regions

• • • • • • • • • • • • • • • • 13q12.12

  well as clustering of rare variants -based on relevant gene functions -to obtain mechanistically coherent subgroups of individuals. Despite this complexity, molecular risks and psychiatric conditions effects on connectivity do highlight convergence on the thalamus and the somatomotor network. Such findings open optimistic avenues to delineate general mechanisms -amenable to intervention -across conditions and genetic risks.

  CNVs carriers and controls'Genetics-first' cohorts were recruited based on the presence of a CNV, regardless of symptomatology, four consortia (two out of four have never been published before): the Simons Variation in Individuals Project (VIP) consortium data (16p11.2 and 1q21.1 CNVs carriers) (40), the University of California, Los Angeles (22q11.2 CNVs carriers), the Montreal rare genomic disorder family project (MRG, CHU Sainte-Justine, Montreal, Canada), and the Define neuropsychiatric-CNVs Project (Cardiff, UK) (see Supplementary Materials and Methods for individual dataset descriptions).

.

Figure 2 .

 2 Figure 2. Effect sizes across idiopathic conditions, CNVs, PGS, and traits.

  Abbreviations: ASD: autism spectrum disorder, SZ: schizophrenia, MDD: major depressive disorder,

  Abbreviations: CCC: concordance correlation coefficient, CI: confidence interval, ASD: autism

Figure 4 .

 4 Figure 4. Effect size of dysconnectivity across 12 functional networks for CNVs, PGS, psychiatric

Figure 5 .

 5 Figure 5. Atlas of functional connectivity relationships across psychiatric conditions, genetic risks

Figure 6 .

 6 Figure 6. Relationship between thalamic dysconnectivity, transcription modules and brain cell types.

.

••

  Connectomes MIST64 Unselected population UKBB • CNV carriers • Non-carriers • Polygenic scores • Morphometry traits • Cognitive traits Connectome-wide association: 36 Functional Connectivity (FC) profiles • 7 neuropsychiatric CNVs + 9 neutral CNVs • Polygenic scores of 4 psychiatric conditions + 3 traits + 3 non-brain related conditions • 4 psychiatric conditions • 3 cognitive traits • 3 brain morphometry traits Clinical cohorts MRG, UCLA, Cardiff, SVIP searchlight • CNV carriers • non-carriers Idiopathic psychiatric cohorts ABIDE1, CNP, SZ, ADHD200 • Cases with ASD, SZ, ADHD, BIP • Controls Pleiotropy Concordance between • FC correlation (r FC ) • Genetic correlation (r G ) • Transcriptomic correlation (r T ) Polygenicity Relationship between effect-sizes of genetic risk on FC and • Cognition and risk for disease • Effect normalized per number of genes in CNVs Whole brain connectivity correlation (rFC) G = r T = r FC r G < r T < r FC Clusters of FC similarities • FC Networks driving clusters :

9 - 2 P

 92 71 (95% CI, 0.45-0.86) CCC= 0.67 (95% CI, 0.12-0.90) CCC= 0.76 (95% CI, 0.41-0.91) CCC= 0.74 (95% CI, 0.56-0.86) P e r f e c t c o n c o r d a n c e P e r f e c t c o n c o r d a n c e P e r f e c t c o n c o r d a n c e Lee, P. H. et al. (2019) CDG of the PGC et al. (2013) Sniekers, S. et al. (2017) Nagel, M. et al. (2018) Grasby, K. L. et al. (2020) G A S 5 ( S N , G P ) M 1 7 -P A X I P 1 ( C b C x ) M 2 4 -P O G Z ( C b C x , B G ) M 1 2 -S L C 6 A 3 ( S N , V T A ) M 1 4 -T L E 6 ( H y ) M 9 -P G A P 1 ( H p , A m g , H y ) M 1 5 -N E F H ( D C b N , B S ) M 1 9 -V D A C 2 ( T h a l, C b N ) M 7 -N G E F ( S t r , N c x , A m g ) M 3 -K C N A B 2 ( H p , T h a l) M 4 -G A B A R A P L 1 ( T h a l-C o r t ) M 6 -M E F 2 C ( N c x , C l) M 1 0 -P D E 1 B ( S t r ) M 2 0 -B 3 G A T 1 ( N C x , B G , v T h a l) M 1 -G A B R B 3 ( T e l) M 1 1 -N T N G 1 ( d T h a l) M 3 0 -V A M P 3 ( v T h a l, G P ) M 3 2 -S L C 2 5 A 1 8 ( S tr , A m g , S N ) e r ( L A M A 2 ) In 6 ( P V A L B ) E x 8 ( N R 4 A 2 ) In 3 ( S H IS A 8 ) In 1 ( C N R 1 ) In 4 ( R E L N ) A s t ( S L C 4 A 4 ) S S T E n d ( C L D N 5 ) M ic ( D O C K 8 ) O P C ( L H F P L 3 ) E x 3 ( N E F M ) E x 5 ( H S 3 S T 2 ) E x 1 ( C B L N 2 ) E x 4 ( T S H Z 2 ) O li ( M B P )

Table 2 )

 2 . 84 out of 190 pairs of FC profiles showed correlations above what is expected by chance and 48 survived FDR (10,000 null correlations, Figure

Table 1

 1 Data demographics.

	Legend: CNV carriers, individuals with idiopathic psychiatric conditions, and controls after MRI
	quality control. Chr: chromosome number, coordinates are presented in Megabases (Mb, Hg19).
	n = tot /Clin: total number of participants /number of participants clinically ascertained. Age (in
	years); M: male; Motion: framewise displacement (in mm). Quantitative variables are expressed as
	the mean ± standard deviation. All sites scanned controls and sensitivity analyses were performed to
	investigate the potential bias introduced by differences in scanning site, age, and sex. IQ loss: mean
	decrease in IQ points associated with each CNV (10, 11). Odd-ratios (OR) for the enrichment of CNVs
	in ASD and schizophrenia were previously published (25-27, 67-72). OR for the enrichment of CNVs
	in ADHD were not available.

The 9 non-psychiatric CNVs (light grey font) were defined as variants without any previous association with psychiatric conditions in large cases control studies

[START_REF] Moreno-De-Luca | Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts[END_REF][START_REF] Sanders | Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci[END_REF](26)[START_REF] Jønch | 15q11.2 Working Group, Estimating the effect size of the 15Q11.2 BP1-BP2 deletion and its contribution to neurodevelopmental symptoms: recommendations for practice[END_REF]

, and detailed information relative to diagnosis, IQ, and motion, are available in Supplementary results. The 3 non-brain-related PGS are in a light grey font. We excluded connectomes of subjects scanned before 2018 (n=11,331) for PGS-SA and PGS-CT because they were used in the discovery GWAS. Abbreviations: DEL: deletion; DUP: duplication; SZ: schizophrenia, ASD: Autism Spectrum Disorder; ADHD: Attention-Deficit / Hyperactivity-Disorder, BIP: Bipolar disorder, MDD: Major Depression Disorder, CrossD: Cross-disorder, LDL: Low-Density Lipoprotein, IBD: Inflammatory Bowel Disease, CKD: Chronic Kidney Disease; SA: Surface Area, CT: Cortical Thickness, CNP: Consortium for Neuropsychiatric Phenomics, MRG: Montreal rare genomic disorder; IQ: intelligence quotient. Disorder; ADHD: Attention-Deficit / Hyperactivity-Disorder, BIP: Bipolar disorder, MDD: Major Depression Disorder, CrossD: Cross-disorder, LDL: Low-Density Lipoprotein, IBD: Inflammatory Bowel Disease, CKD: Chronic Kidney Disease; SA: Surface Area, CT: Cortical Thickness, IQ: intelligence quotient. c) Methods flowchart. Abbreviations: CNV: copy number variation; FC: functional connectivity; ASD: Autism Spectrum Disorder; SZ: schizophrenia; CNP: Consortium for Neuropsychiatric Phenomics, MRG: Montreal rare genomic disorder.

  ). Red = overconnectivity; blue underconnectivity. color scale represents the beta value (z-score).

			No GSA		Global signal adjustment
	CNV (hg19) Metrics 1q21.1 n (genes) / Gene Mean shift -0.02	Status pval shift ns	n tot /clin Connections pos neg 1 11	Age Beta values Top-dec Sex (F/M) pval β values effect min max -1.07 0.62 0.44 0.002	Motion	Frames	Sites	Cohorts	IQ loss OR ASD OR SZ
		2q13	0.04	ns	0	0	-0.15	0.19	0.11	ns
	C-D: Heatmaps showing spatial relationship (Pearson correlation) between 20 FC-profiles and 15q11.2 15: 22.81-23.09 4 CYFIP1 DEL 103/0 64.3 (7) 55/48 0.19 (0.06) 389 (118) DUP 136/0 63.7 (7) 76/60 0.19 (0.05) 393 (118) CNV 13q12.12 0.03 ns 0 0 -0.54 0.5 0.34 ns 15q11.2 0.04 ns 1 0 -0.29 0.36 0.2 0.01 Deletion 16p11.2 0.18 0.04 124 149 -0.98 1.67 0.57 <2e-4	3 0.9	0.3 -	1.9 -
	patterns of gene expression for C) AHBA co-expression module eigengenes (AHBA Nat Neuro 2015), 15q13.3 15: 31.08-32.46 5 CHRNA7 DUP 190/0 64.4 (7) 103/96 0.19 (0.05) 375 (127) 17p12 -0.11 ns 0 0 -0.67 0.46 0.40 ns 22q11.2 -0.25 0.04 4 13 -1.48 1 0.65 <2e-4	0.9	0.7	1.24
	D) CellType marker genes (29, 31); and CellType categories with marker gene name and AHBA TAR -0.08 ns 0 0 -0.48 0.51 0.28 ns
	2q13 modules with eigengene and anatomical associations are included in column names. Rows and 3 DEL 183/0 63.1 (7) 110/73 0.19 (0.05) 370 (123) 1q21.1 0.30 0.008 4 0 -0.62 0.84 0.48 0.002 columns are clustered using k-means. Significance (nominal p-values < 0.05) for the correlation, obtained using 10000 BrainSMASH surrogate profiles, are shown with "x"; LabelShuffle are shown 2: 110.86-110.98 NPHP1 DUP 88/0 64.7 (8) 43/45 0.19 (0.05) 399 (98) 16p13.11 16: 15.51-16.29 6 MYH11 DUP 40/0 64.7 (6) 21/19 0.18(0.04) 298 (101) CNV 2q13 -0.03 ns 0 0 -0.34 0.26 0.18 ns 13q12.12 0.31 0.009 0 0 -0.53 0.48 0.31 ns 15q11.2 -0.06 ns 0 0 -0.24 0.24 0.16 0.04 15q13.3 0.007 ns 0 0 -0.20 0.18 0.11 ns Duplication with "+"; and Both with "*". 13q12.12 5 DEL 22/0 63.5 (6) 12/9 0.20(0.07) 391 (111) 16p11.2 -0.07 ns 4 3 -1.04 0.55 0.38 0.002 16p13.11 -0.16 0.03 0 0 -0.42 0.40 0.26 ns	3	UKBB	1.8 0.6 2 2.1	--0.5 -	--1.5 -
	13: 23.56-24.88 Cross Dis SPATA13 22q11.2 0.24 -0.002	0.03 DUP ns	0 23	20/0 2 22	60.8 (7) -0.78 -.02	0.69 .03	10/10 0.43 0.01 <2e-4 0.04	0.20(0.06) 361 (125)	0.6	-	-
	17p12 17: 14.14-15.43 Schizophrenia -0.005 4 CMT1A ASD 0	DEL ns • Rs-fMRI Preprocessing 16/0 3 1 ns 30 27 • Quality control	66 (7) -.02 .02	.02 .03	8/8 0.01 0.01 <2e-4 ns	0.2 (0.05)	368 (117)	1.8	-	-
	TAR 1: 145.39-147.39 1q21.1 1: 146.53-147.39 score Polygenic Surface area 0.002 15 RBM8 MDD 0 IQ 0.004 LDL -0.003 IBD 0 7 CHD1L CKD 0	DUP DEL ns ns ns ns DUP ns ns	6 74 0 1 0 18	29/0 25/15 21 42 0 1 19/6 0 20	59.8 (7) 44.4 (19) -.02 -.02 -.02 -.02 0.02 .02 .02 .02 50.9 (19) -.02 .02 -.03 .03	14/15 12/13 0.01 0.003 0.01 <2e-4 0.009 ns 0.01 ns 13/6 0.01 ns 0.02 0.0008 0.18 (0.07) 271 (172) 0.17(0.05) 410 (95) 0.21 (0.08) 276 (188))	6 7	UKBB-MRG-Cardiff-SFARI	2.4 15 25	-3.2 5.3	-6.4 2.9
	22q11.2 22: 19.04-21.47 Thickness 49 TBX1 16p11.2 27 Psychiatric ASD Schizophrenia -0.21 <2e-4 DEL 0.002 ns DUP DEL -0.09 0.01 Bipolar -0.16 0.08 conditions ADHD 0.04 0.21	0 21 208 22 0	43/43 0 22/12 32/28 11 242 12 0	16.9 (7) -.02 39.4 (23) 21.7 (20) -0.35 0.42 .02 -0.41 0.5 -0.67 0.63 -0.22 0.22	19/24 0.01 12/10 13/19 0.21 <2e-4 ns 0.30 <2e-4 0.42 <2e-4 0.15 <2e-4	0.18 (0.07) 0.19 (0.09) 225 (163) 120 (36) 0.22 (0.09) 108 (99)	1 5 5	UCLA UCLA-UKBB Cardiff-MRG SFARI -MRG	28.8 8.3 26	32.3 2 14.3	23 0.2 1.1
	16: 29.65-30.20 Idiopathic KCTD13 Psychiatric Morpho Thickness 0.01 <2e-4 DUP ASD SZ Surface area 0.03 <2e-4 metry Volume 0.03 <2e-4 BIP Cognitive Fluid Intel 0.01 0.0004 G-Factor 0.02 <2e-4 scores Neuroticism -0.01 0.002	557 711 704 315 340 208	35/29 225 283 525 734 716 44 286 288 205	34.1 (19) 16 (6.6) 33.9 (9.2) -.06 .06 -.11 .13 -.13 -.12 35 (9) -.04 .04 -.06 .05 -.03 .04	14/21 0/225 73/210 0.04 <2e-4 0.07 <2e-4 0.07 <2e-4 20/24 0.02 <2e-4 0.03 <2e-4 0.02 <2e-4	0.21 (0.09) 148 (140) 0.18 (0.05) 139 (52) 0.17 (0.06) 147 (50) 0.17(0.07) 127 (25)	4 12 12 2	-UKBB ABIDE1 Montreal-SZ CNP CNP	11 ---	10.5 ---	11.7 ---
	Conditions	ADHD		226	15 (9.4)		66/160		0.15 (0.04)	135 (23)	8	ADHD-200 CNP	-	-	-
	Polygenic	SZ, ASD, MDD, IQ, CrossD, LDL, CKD, IBD	29622 64.2 (7.5) 15922/13700 0.18 (0.05) 388 (116)	-	-	-
	scores
			SA CT			17885 65.1 (7.4)	9646/8239 0.18 (0.04) 390 (114)	-	-	-
			G-factor		20005 64.4 (7.4) 10732/9273 0.18 (0.04) 391 (113)	3	UKBB	-	-	-
	Cognition		Fluid intelligence		27683	64 (7.5)	14859/12824 0.18 (0.05) 388 (115)	-	-	-
	Morphometry		Neuroticism		24168	64 (7.5)	12792/11376 0.18 (0.04) 388 (115)	-	-	-
			SA, CT, Volume		28207 64.1 (7,5) 15191/13016 0.18 (0.05) 388 (115)	-	-	-
			UKBB			30185 64.1 (7.5) 16260/13925 0.19 (0.05) 387 (116)	3	UKBB	-	-	-
			SFARI			84	26.7 (15)	35/49		0.18 (0.07) 81.4 (19)	2	SFARI	-	-	-
			MRG			39		34 (16)		25/14		0.21 (0.07) 381 (177)	1	MRG	-	-	-
	Controls
			Cardiff			8	39.8 (4)		4/4		0.12 (0.06) 151.4 (35)	1	Cardiff
			UCLA			43	13 (4.6)		22/21		0.14 (0.04)	130 (24)	1	UCLA	-	-	-
			Psychiatric cohorts		848	21.2 (11)	255/593		0.15 (0.05)	155 (46)	31	-	-	-	-

CellTypes categories include five interneuron subtypes (i.e., Somatostatin (SST), In1, In3, In4, and In6), five excitatory neuron subtypes (i.e., Ex1, Ex3, Ex4, Ex5, and Ex8), and six non-neuronal subtypes (Ast: Astrocytes; End: Endothelial; Mic: Microglia; Oli: Oligodendrocytes; OPC: oligodendrocyte precursor cells; and Per: Pericytes). Ex: Excitatory; In: Inhibitory. Abbreviations: ASD: autism spectrum disorder, SZ: schizophrenia, BIP: bipolar disorder, MDD: major depressive disorder, NT: Neuroticism, PGS: Polygenic score, Del: deletion, Dup: duplication, Fluid intel: fluid intelligence, SA: surface area, CT: cortical thickness. ABHA Module Anatomy abbreviations: Amg: Amygdala; BG: Basal Ganglia; BS: BrainStem; CbCx: Cerebellar Cortex; CbN: Cerebellar Nuclei; Cl: Claustrum; DCbN: Deep Cerebellar Nuclei; dThal: Dorsal Thalamus; Hp: Hippocampus; Hy: Hypothalamus; Ncx: Neocortex; SN: Substantia Nigra; Str: Striatum; Tel: Telencephalon; Thal: Thalamus; Thal-Cort: Thalamocortical; vThal: Ventral Thalamus; VTA: Ventral Tegmental Area. DEL: deletion; DUP: duplication.

.

Acknowledgments

Data and materials availability

Data from UK Biobank was downloaded under the application 40980, and can be accessed via their standard data access procedure (see http://www.ukbiobank.ac.uk/register-apply). UK Biobank CNVs were called using the pipeline developed in Jacquemont Lab, and described in https://github.com/labjacquemont/MIND-GENESPARALLELCNV. The final CNV calls are available from UK Biobank returned datasets (Return ID: 3104, https://biobank.ndph.ox.ac.uk/ukb/dset.cgi?id=3104 ). ABIDE1, COBRE, ADHD200, CNP, 16p11.2 SVIP data are publicly available: http://fcon_1000.projects.nitrc.org/indi/abide/abide_I.html , http://schizconnect.org/queries/new , http://fcon_1000.projects.nitrc.org/indi/adhd200/ , https://www.openfmri.org/dataset/ds000030/ , https://www.sfari.org/funded-project/simonsvariation-in-individuals-project-simons-vip/. The 22q11.2 UCLA raw data are currently available by request from the PI. Raw imaging data for the Montreal rare genomic disorder family dataset is going to be available on the LORIS platform in 2022. The Cardiff raw data is not publicly available yet, contact the PI for further information. Allen Human Brain Atlas is available from https://human.brain-map.org/. Single-cell expression data is available from the National Center for Biotechnology Information under the accession code GSE97942 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97942 ). All processed connectomes are available through a request to the corresponding authors. Code for all analyses and visualizations, beta values, and p-values for the 36 FC-profiles are available online through the GitHub platform with Jupyter notebook: https://github.com/claramoreau9/NeuropsychiatricCNVs_Connectivity


Marion Rambault

Emilie Doz-Deblauwe

Yves Le Vern

Florence Carreras

Patricia Cunha

Pierre Germon

Pascal Rainard

Nathalie Winter

Aude Remot

Neutrophils encompass a regulatory subset suppressing T cells at steady state in cattle

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Neutrophils in cattle

Neutrophils are major partners of the innate immune system and are considered the first line of defense against microbes. These phagocytes recognize invading microbes and produce large amounts of reactive oxygen species into phagocytic vacuoles to kill microbes. New roles for neutrophils bridging innate and adaptive immunity have also emerged. Neutrophils play an essential role in the control of infectious diseases in cattle, especially mastitis. As a biomarker, they are also the major indicator of inflammation during subclinical mastitis. Neutrophils are the main cell type responsible for the rise of milk somatic cell count during mammary infection.

Plasticity of neutrophils

Despite the major importance of neutrophils in bovine infectious diseases, neutrophils are also a double-edged sword. If their recruitment is not controlled, they can cause tissue damage. New populations of neutrophils have been recently described with role in regulation of inflammation and adaptative immune response. Although neutrophils diversity and plasticity are now well documented for mouse and human neutrophils, they remain unknown for domestic species including cattle. Here, we discovered and characterized a new population of regulatory neutrophils that displayed suppressive activity on T-cells at steady state in cattle.

  Neutrophils encompass a regulatory subset suppressing T cells at steady state in cattle Centre de recherche Val de Loire UMR1282 Infectiologie et Santé Publique MHC-II pos neutrophils produce higher levels of ROS than MHC-II neg but similarly phagocytose bioparticles and kill bacteria ns MHC-II pos neutrophils are enriched in lymphoid organs MHC-II neg and MHC-II pos neutrophils display cells were incubated 1h with LPS and labelled with annexin-V and propidium iodide (PI) to analyze apoptotic and dead cells

  phagocytosis, production of ROS and killing of MHC-II neg and MHC-II pos neutrophils was measured after purification by cells sorting and using E.coli bioparticules pHrodo, CellROX detection kit (flow cytometry) and E.coli P4 strain respectively. Real Time PCR (Fluidigm biomark) mRNA expression normalized to the expression of three housekeeping genes to calculate the ΔCt values. Hierarchical clustering of gene expression performed on median centered ΔCt values, using the ward.D2 method. For each gene, ΔCt values were centered to the median ΔCt value. n=8 for MHC-II neg ; n=6 for MHC-II pos prepared from blood, bone marrow (BM), tracheobronchial lymph nodes (LN) and spleen, then labelled with anti-G1 and anti-MHCcharacterized a new subset of regulatory neutrophils, able to suppress T cells. Next, we will investigate how they behave during clinical conditions such as mastitis, which remains one of the most important issue in dairy farming.