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Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic—rFunctional connectivity = 0.71 [0.40–0.87] and rTranscriptomic—rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms—amenable to intervention—across psychiatric conditions and genetic risks.

BACKGROUND: Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits.METHODS: Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects).RESULTS: Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10-6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10-6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease.CONCLUSIONS: Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.

Polygenicity and pleiotropy are key properties of the genomic architecture of psychiatric disorders. An optimistic interpretation of polygenicity is that genomic variants converge on a limited set of mechanisms at some level from genes to behavior. Alternatively, convergence may be minimal or absent.We took advantage of brain connectivity, measured by resting-state functional MRI (rs- fMRI), as well as rare and common genomic variants to understand the effects of polygenicity and pleiotropy on large-scale brain networks, a distal step from genes to behavior. We processed ten rs-fMRI datasets including 32,988 individuals, to examine connectome-wide effects of 16 copy number variants (CNVs), 10 polygenic scores, 6 cognitive and brain morphometry traits, and 4 idiopathic psychiatric conditions.Although effect sizes of CNVs on connectivity were correlated to cognition and number of genes, increasing polygenicity was associated with decreasing effect sizes on connectivity. Accordingly, the effect sizes of polygenic scores on connectivity were 6-fold lower compared to CNVs. Despite this heterogeneity of connectivity profiles, multivariate analysis identified convergence of genetic risks and psychiatric disorders on the thalamus and the somatomotor network. Based on spatial correlations with transcriptomic data, we hypothesize that excitatory thalamic neurons may be primary contributors to brain alteration profiles shared across genetic risks and conditions. Finally, pleiotropy measured by genetic and transcriptomic correlations between 38 pairs of conditions/traits showed significant concordance with connectomic correlations, suggesting a substantial causal genetic component for shared connectivity.Such findings open avenues to delineate general mechanisms - amenable to intervention - across conditions and genetic risks.One sentence summaryEffects of rare and common genomic variants on brain functional connectivity shed light on the impact of polygenicity and pleiotropy in psychiatry.

BackgroundCopy Number Variants (CNVs) associated with autism and schizophrenia have large effects on brain anatomy. Yet, neuroimaging studies have been conducted one mutation at a time. We hypothesize that neuropsychiatric CNVs may exert general effects on brain morphometry because they confer risk for overlapping psychiatric conditions.MethodsWe analyzed T1-weighted MRIs and characterized shared patterns on brain anatomy across 8 neuropsychiatric CNVs. Clinically ascertained samples included 1q21.1 (n=48), 16p11.2 (n=156), or 22q11.2 (n=96) and 331 non-carriers. Non-clinically ascertained samples from the UK Biobank included 1q21.1 (n=19), 16p11.2 (n=8), 22q11.2 (n=9), 15q11.2 (n=148) and 965 non-carriers. Canonical correlation analysis (CCA) and univariate models were used to interrogate brain morphometry changes across 8 CNVs.ResultsEight CNVs affect regional brain volumes along two main gene-morphometry dimensions identified by CCA. While fronto-temporal regions contributed to dimension 1, dimension 2 was driven by subcortical, parietal and occipital regions. Consistently, voxel-wise whole-brain analyses identified the same regions involved in patterns of alteration present across the 4 deletions and duplications. These neuroanatomical patterns are similar to those observed in cross-psychiatric disorder meta-analyses. Deletions and duplications at all 4 loci show mirror effects at either the global and/or the regional level.ConclusionNeuropsychiatric CNVs share neuroanatomical signatures characterized by a parsimonious set of brain dimensions. The latter may underlie the risk conferred by CNVs for a similar spectrum of neuropsychiatric conditions.

Rare genomic Copy Number Variants (CNVs) are major contributors to neurodevelopmental disorder. The vast majority of pathogenic CNVs reported back to patients are ultra-rare and their quantitative effects on traits such as intelligence are undocumented. Here, we identified all CNVs ≥ 50 kilobase in 24,092 individuals from unselected and autism cohorts. We developed statistical models to estimate the effect-size of CNVs on intelligence based on their coding and non-coding characteristics. Measures of intolerance to haploinsufficiency best explained the effect of any deletion or duplication on general intelligence. There was no heterogeneity across unselected and autism cohorts. Validation was performed using an intraclass concordance and showed that model estimates of general intelligence were 78% accurate with mean effect-sizes previously published for 47 CNVs. Inheritance data on 27,766 CNVs showed that deletions and duplications with the same large effect-size on intelligence occur de novo at the same frequency. Our first outline for the effect sizes of all coding genes on intelligence suggests that around 10,000 genes affect this trait.

Large effect-size mutations such as copy number variants (CNVs) have the potential to provide key insights into the underlying biological mechanisms linking deleterious genetic variants to brain architecture and neuropsychiatric disorders. To date, the effect of CNVs on functional brain connectivity remains mostly unstudied, and findings are derived from analyses conducted one mutation at a time. The lack of systematic cross-CNV comparisons hinders our understanding of any potential general mechanisms linking CNVs to effects on brain organization. We performed connectome-wide analyses using resting-state functional MRI data from 436 carriers of neuropsychiatric CNVs at the 1q21.1, 15q11.2, 16p11.2, 22q11.2 loci, and 4 neutral-effect CNVs, 66 carriers of scarcer neuropsychiatric CNVs, 756 individuals with idiopathic autism spectrum disorder (ASD), schizophrenia, attention deficit hyperactivity disorder, and 5,377 controls. Neuropsychiatric CNVs showed global shifts of mean connectivity. The effect size of CNVs on relative connectivity (adjusted for the mean) was correlated with the known level of neuropsychiatric risk conferred by CNVs. We reported architecture similarities between individuals with idiopathic psychiatric conditions and high-risk neuropsychiatric-CNVs, predominantly in the thalamus, the posterior cingulate cortex, and the anterior insula. We identified a linear relationship between connectivity and intolerance to haploinsufficiency measured for all genes encompassed by CNVs across 18 loci. This profile involved the thalamus, the basal ganglia, somatomotor and frontoparietal networks and was correlated with lower general intelligence and higher autism severity scores. An exploratory factor analysis confirmed the contribution of these regions to three latent components shared across CNVs and neuropsychiatric disorders. We posit that deleting genes intolerant to haploinsufficiency reorganize connectivity along general dimensions irrespective of where deletions occur in the genome. This haploinsufficiency brain signature opens new avenues to understand polygenicity in psychiatric conditions and the pleiotropic effect of CNVs on cognition and risk for neuropsychiatric disorders.

Le syndrome d’interruption de la tige hypophysaire (PSIS) est un désordre rare qui affecte la fonction du système endocrinien. Jusqu’à nos jours, l’imagerie par la résonance magnétique (IRM) demeure la méthode la plus entreprise afin d’évaluer in vivo l’anomalie d’organogenèse de la tige hypophysaire chez les patients. L’absence de la tige caractérise une déficience permanente en hormone de croissance (GH) pendant que l’étiologie du syndrome demeure inconnue. PSIS se définit comme l’hypopituitarisme congénital et il se caractérise soit par une ectopique post- hypophysaire, soit par une hypoplasie antéhypophysaire, ou encore par une hypoplasie de la tige hypophysaire. Notre objectif consiste à déterminer les mutations génétiques partagées entre les sujets affectés de l’étude et qui pourraient expliquer les causes du syndrome. Pour y parvenir, nous avons analysé les données de séquençage d’exomes (WES) provenant de sept familles caucasiennes simplex, une famille d’origine arabique et cinq autres dont la généalogie est incomplète. Ces données ont été précédemment analysées, pour le même but, par d’autres membres de l’équipe en utilisant le pipeline bio-informatique standard basé sur l’utilisation du logiciel GATK. Nous avons préférentiellement opté pour une nouvelle analyse en utilisant deux différents pipelines bio-informatiques indépendants, pour ensuite comparer conjointement les résultats obtenus. Notre protocole consiste à assembler : d’abord, deux pipelines alternatifs de détection de mutations génétiques ponctuelles (SNV). Ils sont composés d’un logiciel d’alignement de séquence (Bowtie2) et deux logiciels d’appel de variantes (Freebayes et SAMtools). Ensuite, nous avons assemblé trois pipelines de détection de variations du nombre de copies (CNV) génomiques composés communément d’un logiciel d’alignement de séquence (BWA) et trois logiciels d’appel de CNV (CoNIFER, fishingCNV, xHmm). Nos résultats nous ont permis d’identifier des mutations candidates additionnelles qui n’ont jamais été identifiées. De plus, notre méthodologie nous a permis de caractériser certains résultats faux positifs, par conséquent elle pourra nous aider à améliorer la performance des pipelines de détection de variations génomiques existantes., Pituitary stalk interruption syndrome (PSIS) is a rare disorder that affects the function of the endocrine system of the affected individuals. The absence of the pituitary stalk, assessed by MRI, characterizes patients with permanent growth hormone deficiency while the etiology of the syndrome remains unknown. PSIS is defined by clinical hypopituitarism together with anatomical findings including a hypoplastic anterior pituitary, ectopic posterior pituitary and reduced or hypoplastic pituitary stalk. We aim to find shared variations (SNP, CNV) among affected patients in coding regions which could explain the origin of the syndrome. We analyzed the exome NGS data from 8 affected French Canadian trio families, with one additional consanguineous Arabic trio family and 5 families with incomplete pedigree. These data were previously analyzed, for the same objective, by other members of the team using a standardize GATK based bioinformatics pipeline. It was desired to reanalyze the complete data set with two other independent pipelines, followed by a comparison of the SNP discovery results. In the present aspect of the study, we built two SNP discovery pipelines, both composed of a different NGS data aligners (Bowtie2) and each composed a different variant caller (Freebayes, SAMtools), then a CNV discovery pipeline which is composed of three different CNV callers (CoNIFER, fishingCNV, xHmm). In addition to the candidate mutations identified in the previous analysis, we identified additional candidate mutations which had not been detected and never been reported. Furthermore, our method helps to discover the sources of variation false discovery which could help to improve existing genomic mutation discovery pipelines.

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging genetics field has struggled to integrate all these modalities to investigate the interplay between genetic blueprint, environment, human health, daily living skills and outcomes. Hence, we interrogated the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and corresponding implications on cognitive, behavioral, psychosocial, and socioeconomic traits. Specifically, we designed a holistic pattern-learning algorithm that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 962 behavioral variables spanning 20 categories in 7,657 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks, and specific parameters of the family well-being (increased parental and child stress, anxiety and depression) or neighborhood dynamics (decreased safety); effects extending beyond the impairment of cognitive ability or language capacity, dominantly reported in the CNV literature. Our investigation thus spotlights a far-reaching interplay between genetic variation and subjective life quality in adolescents and their families.

Rare copy number variants (CNVs) and polygenic risk for intelligence (PRS-IQ) both confer risk for autism spectrum disorder (ASD) but have opposing effects on cognitive ability. The field has struggled to disentangle the effects of these two classes of genomic variants on cognitive ability from their effects on ASD risk, in part because previous studies did not include controls with cognitive measures. We aim to investigate the impact of these genomic variants on ASD risk while adjusting for their known effects on cognitive ability. In a cohort of 8,426 subjects with ASD and 169,804 controls with cognitive assessments, we found that rare coding CNVs and PRS-IQ increased ASD risk, even after adjusting for their effects on cognitive ability. Bottom decile PRS-IQ and CNVs both decreased cognitive ability but had opposing effects on ASD risk. Models combining both classes of variants showed that the effects of rare CNVs and PRS-IQ on ASD risk and cognitive ability were largely additive, further suggesting that risk for ASD is conferred independently from its effects on cognitive ability. Despite imparting mostly additive effects on ASD risk, rare CNVs and PRS-IQ showed opposing effects on core and associated features and developmental history among subjects with ASD. Our findings suggest that cognitive ability itself may not be the factor driving the underlying risk for ASD conferred by these two classes of genomic variants. In other words, ASD risk and cognitive ability may be two distinct manifestations of CNVs and PRS-IQ. This study also highlights the challenge of understanding how genetic risk for ASD maps onto its dimensional traits., Competing Interests: CONFLICT OF INTEREST The authors have no conflicts of interest to declare.

Asymmetry between the left and right brain is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variant studies, which typically exert small effects on brain phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We quantitatively dissected the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior mapping highlights the consequences of genetically controlled brain lateralization on human-defining cognitive traits.

Objectives: Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs) including autism (ASD) and schizophrenia (SZ). Overall, little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, we investigated gross volume, and vertex level thickness and surface maps of subcortical structures in 11 different CNVs and 6 different NPDs., Methods: Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (at the following loci: 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2) and 782 controls (Male/Female: 727/730; age-range: 6-80 years) as well as ENIGMA summary-statistics for ASD, SZ, ADHD, Obsessive-Compulsive-Disorder, Bipolar-Disorder, and Major-Depression., Results: Nine of the 11 CNVs affected volume of at least one subcortical structure. The hippocampus and amygdala were affected by five CNVs. Effect sizes of CNVs on subcortical volume, thickness and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and SZ. Shape analyses were able to identify subregional alterations that were averaged out in volume analyses. We identified a common latent dimension - characterized by opposing effects on basal ganglia and limbic structures - across CNVs and across NPDs., Conclusion: Our findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions. We also observed distinct effects with some CNVs clustering with adult conditions while others clustered with ASD. This large cross-CNV and NPDs analysis provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD, as well as why a single CNV increases the risk for a diverse set of NPDs.