Dissecting Autism and Schizophrenia Through Neuroimaging Genomics

Clara A. Moreau; Armin Raznahan; Pierre Bellec; Mallar Chakravarty; Paul M. Thompson; Sebastien Jacquemont;

Disclosures

Brain. 2021;144(7):1943-1957. 

In This Article

Bottom-up Approach: Large Effect Genetic Variants to Dissect Mechanisms in Psychiatry

The relevance of conducting bottom-up studies emerged from the questions raised by genetic discoveries of top-down studies. First, why are mutations overrepresented in individuals with a psychiatric diagnosis, and are they related to core symptoms of the disease or to comorbidities? Second, why are mutations associated with several diagnoses (pleiotropy), and do they impact a single dimension contributing to all diagnoses?

By contrast to the top-down approach, the bottom-up recruitment based on the presence of a genetic risk factor for neuropsychiatric disorders (Figure 4), allows for the investigation of pathways related to a particular biological risk for psychiatry irrespective of any clinical phenotype. The statistical power required to conduct bottom-up studies limits this approach to genetic variants with large enough effect size and population frequency. Clinical routine investigation using whole-genome chromosomal microarrays revealed that CNVs are present in 10–15% of children with neuropsychiatric disorders.[104] Many recurrent neuropsychiatric CNVs have large effect sizes (~1 Cohen's d; Figure 2) on cognitive and neuroimaging traits and are natural candidates to conduct genetic first studies.

Figure 4.

Top-down versus bottom-up approaches. The genetic-first, bottom-up approach (right) can build models/signatures from a lower level in the hierarchy (e.g. intermediate brain phenotype), and then asks questions about how such low-level models can explain observations higher up in the hierarchy (clinical manifestations).

Deep Phenotyping one Mutation at a Time

Recurrent CNVs at the 16p11.2 and 22q11.2 loci are among the most frequent high-risk mutations associated with ASD and schizophrenia. Deletions and duplications between breakpoints 4 and 5 on chromosome 16p11.2 were first linked to ASD in 2008.[105] Carriers of the duplication have a higher risk of developing schizophrenia (OR = 9.4).[27,30] Both 16p11.2 deletions and duplications have been enriched in a broad spectrum of other conditions including ADHD and intellectual disability.[106] Genetic first studies have estimated effect sizes of −1.5 Cohen's d on IQ, and −1.4 Cohen's d on phonological memory for deletions.[107,108] A smaller decrease in IQ is associated with duplications (−0.8 Cohen's d). Both CNVs do also affect social responsiveness (−3 Cohen's d), as well as gross and fine motor skills.[73,109] Mirror anthropometric phenotype has been reported with deletions mainly associated with obesity and macrocephaly and duplications associated with underweight, and microcephaly. Again, effects are large ranging from 0.8 to 1 Cohen's d.[107,108,110,111] Neuroimaging analyses reported negative gene-dosage effects on total brain volume, total grey and white matter with again similar large effect sizes.[112] Once effects on global volumes are taken into account, a mirror negative gene dosage effect was observed for the insula volume.[73] Other altered regions were only observed in deletions: transverse temporal gyrus, the calcarine cortex (Cohen's d > 1), superior and middle temporal gyrus (Cohen's d < −1) or in duplications: caudate and hippocampus (control> duplication, −0.5 > Cohen's d > −1).[73] At the functional level, a negative gene dosage effect on global connectivity was identified. After accounting for global signal, regional alterations in deletion included a thalamic-sensorial overconnectivity, impairment of frontoparietal network with temporoparietal regions, and strong disturbance of the posterior insula, the presupplementary motor cortex, and the basal ganglia (beta values from −0.8 to 1.4 z-scores).[44,113] Duplications had a smaller effect on connectivity and mostly involved the amygdala-hippocampus complex, the cerebellum, and the basal ganglia.

Deletion at the 22q11.2 locus is the largest risk factor for schizophrenia (OR = 68) and up to 30% of adolescents and adults will develop psychosis.[27,114,115] Children with 22q11.2 deletion have also a high risk of developing ASD (OR = 32),[30] ADHD, and anxiety disorders.[116,117] Duplications are less severe and are inherited in 70% of the cases (compared to deletions which are de novo in over 90% of individuals). While studies suggested a protective effect for schizophrenia[118] (OR = 0.15),[27] the duplication has been associated with a wide range of phenotypes, including ASD, psychomotor development, speech delay, and cognitive deficits.[118] Ascertainment bias remains an issue in the study of genomic disorders which are often recruited in the clinic. Although this is particularly true for smaller effect size variants but may also apply to a lesser degree to 22q11.2.[119]

ENIGMA 22q11.2 deletion T-weighted studies reported a global decrease in surface area and an increase in mean cortical thickness (Cohen's d: surface area = 1, cortical thickness = 0.6), particularly in temporal and cingulate cortices.[120] Subcortical analyses showed decreased volumes and abnormal shape of the thalamus, putamen, hippocampus, and amygdala volumes (Cohen's d = −0.9), as well as a greater lateral ventricle volume.[121] The duplication shows an opposing pattern for mean cortical thickness, intracranial volume,[122] and the hippocampus volume. Functional MRI studies have shown mirror effects at the global connectivity level. Underconnectivity between DMN, limbic, and frontoparietal networks is observed in deletions compared to control.[44,123] Studies replicated a thalamocortical overconnectivity involving somatomotor regions and underconnectivity involving default mode network. The opposite effect was observed for the hippocampus in regard to somatomotor and frontoparietal network connectivity.[44,124]

However, it remains unclear if rare variants such as 16p11.2 and 22q11.2 represent mechanistic exceptions or if they may delineate dimensions that are generalized to idiopathic ASD and schizophrenia. This has been investigated at the functional connectivity level. The 16p11.2 deletion connectivity signature showed similarities with individuals diagnosed as either idiopathic schizophrenia or ASD and was associated with higher cognitive and behavioural impairments. Connectivity similarities were driven by the thalamus, the basal ganglia, and the cingulate areas. The 22q11.2 deletion connectivity profile showed similarities with individuals with idiopathic schizophrenia, ASD, and to a lesser extent with ADHD in particular through the thalamus, temporal pole, putamen, and the posterior insula.[44] The thalamus and somatomotor regions played a critical role in dysconnectivity observed across both deletions and idiopathic psychiatric conditions.

Studies have sought to identify major genes driving phenotypic effects in CNV carriers to understand cellular mechanisms that give rise to the risk conferred by these variants. The 16p11.2 chromosomal region contains 29 unique genes and none of them has been formally linked to the 16p11.2 clinical phenotype.[125] However, a smaller critical region of five genes, which includes TAOK2 and KCTD13, has been identified. Animal studies on TAOK2 reported dosage-dependent effects including changes in brain size and neural connectivity.[126] Loss of TAOK2 activity was related to a reduction in RhoA activation, suggesting that this pathway is a mediator of TAOK2-dependent synaptic development. Of note, TAOK2 is interacting with KCTD13 in the RhoA signalling pathway, and with MAPK3.[84] The overexpression of the human KCTD13 gene in zebrafish embryos induces a decrease in head size whereas deletion of the zebrafish orthologue yields a macrocephalic phenotype,[127] but follow-up studies did not replicate KCTD13 findings.[125]

Among the 50 genes within the 22q11.2 locus,114, COMT, TBX1, SEPT5, and DGCR8 were studied as putative critical drivers of the phenotype but results remain inconsistent.[128] Importantly, an excess of de novo loss of function mutations has not been reported in any of the genes within the 16p11.2 and 22q11.2 regions. Overall, these studies show that association evidence for a CNV does not automatically imply that a single or even few genes are driving the effects.[129]

Common and Specific Effects of Genomic Variants on Intermediate Brain Phenotypes

Single variant approaches reported in the previous chapter can only be applied to a few recurrent pathogenic variants frequent enough to establish a case-control study design. Thus, the effects of most rare deleterious variants remain undocumented. Single variant studies are therefore at odds with the extreme polygenicity of schizophrenia and ASD highlighted by GWAS discussed in the top-down approach. Several studies have suggested that every megabase of the genome contains common variation associated with increased risk for schizophrenia. This infinitesimal model also referred to as omnigenic applies to ASD and evidence shows that any megabase deletion including coding genes increases the risk for this condition.[82] In this context, two non-exclusive hypotheses could be pursued: (i) an infinite number of disease-associated variants map onto an infinite number of neuroimaging patterns; and (i) variants converge on a parsimonious set of large scale network alterations. The first hypothesis alone appears improbable because ASD and schizophrenia case-control neuroimaging studies would have otherwise obtained no result.

In the effort to characterize specific and shared effects of CNVs on neuroimaging outcomes, a first cross-genetic study clustered neuroanatomical alterations across 26 different genetic mouse models of autism (including 16p11.2 CNVs, MECP2, NRXN1, and FMR1).[130] Regional differences (relative to total brain volume) were heterogeneous but some regions were recurrently affected across models including the temporoparietal area, the cerebellar cortex, the frontal lobe, the hypothalamus, and the striatum. The authors clustered anatomical alterations and identified three distinct subgroups driven respectively by the limbic system, white matter structures/basal ganglia/thalamus, and cerebellar regions. Knockout mouse models from this study seemed to recapitulate the heterogeneity seen with the imaging findings in autism patients.

Similar studies in humans have been extremely difficult to implement because of the lack of data on individuals with genomic variants. Recent access to neuroimaging genetic data in the UK Biobank enabled the study of 12 schizophrenia-associated CNVs in the general population (n = 49 unaffected CNV carriers with schizophrenia, including 16p11.2, 22q11.2, NRXN1, 15q11.2, and 1q21.1 CNVs).[131] The thalamus, the hippocampus, and the nucleus accumbens showed decreased volumes in CNV schizophrenia carriers. Thalamic and hippocampal volumes appeared to mediate effects on cognitive performances. A functional resting-state study of 502 carriers of eight neuropsychiatric CNVs (22q11.2, 16p11.2, 15q11.2, and 1q21.1 CNVs) showed that deletions and duplications had strong effects on connectivity. The level of brain dysfunction was also associated with the known levels of risk conferred by mutations. Connectivity signatures of 16p11.2 and 22q11.2 deletions showed similarities across several networks involving the frontoparietal, DMN, ventral attentional, and somatomotor networks.[44] Dysconnectivity profiles across eight CNVs and idiopathic ASD, schizophrenia, and ADHD were summarized by three latent components involving the thalamus, the temporal pole, the anterior cingulate, and the ventromedial prefrontal cortex. The level of similarity between CNVs and idiopathic conditions was associated with mutation severity and was driven by the thalamus, and the posterior cingulate cortex, previously identified as hubs in transdiagnostic psychiatric studies (cf. 'Lessons learned from top-down studies' section). Beyond categorical diagnoses, CNV connectivity signatures were correlated with measures of autism severity and IQ.[44]

The extreme polygenicity of ASD and schizophrenia suggests that broad groups of rare and common variants share cognitive effects and neuroimaging patterns. A weighted linear model was developed to estimate the effect of CNVs on IQ using scores of intolerance to protein loss of function in a dataset of 24 000 individuals from unselected and psychiatric cohorts with cognitive assessments.[132–134] These models could predict the effect size of any CNV with 80% accuracy. Deletions of >50% of the coding genome negatively impacted IQ, and this is consistent with infinitesimal/omnigenic models. The same linear weighted model using scores of intolerance to protein loss-of-function was used to explain functional connectivity across CNVs at 18 genomic loci in 502 carriers and 4427 non-carrier individuals. Deletions measured with scores of intolerance to probability of being loss-of-function intolerant (pLI) were associated with a general connectivity signature involving the thalamus, the anterior cingulate cortex, and the somatomotor network. This general deletion signature was correlated with lower general intelligence and higher autism severity scores in unselected, ASD, and ADHD cohorts.

A similar approach showed that schizophrenia relative-risk was correlated with diminished performance on at least one cognitive test. This approach was also applied to 21 carriers of either 22q11.2, 15q11.2, 1q21.1, 16p11.2, and 17q12 CNVs and 15 non-carriers showing that macro and microstructural properties of the cingulum bundles were associated with schizophrenia relative-risk.[135]

Bottom-up approaches have also been conducted in the general population using the aggregated genetic effect of common variants (polygenic risk scores). Polygenic risk scores use a set of trait-related SNPs that may not achieve significance at the individual level but collectively may explain a portion of the trait variance.[136] Negative associations were observed in the general population between schizophrenia-polygenic risk score and mean cortical thickness, insular lobe[137–139] and, frontotemporal cortical thickness as well as left hippocampus volume.[140] This demonstrated again that some neuroanatomical alterations are shared between individuals at risk for schizophrenia and diagnosed with schizophrenia. Of note, studies (Generation R) of polygenic risk scores for ASD, schizophrenia, ADHD, bipolar disorder, and major depression, did not yield any results.[141] This may be due to the fact that as opposed to bottom-up studies of single mutations, polygenic risk scores are likely to be mechanistically heterogeneous, diluting the neuroimaging signal. Computing polygenic scores informed by biological and brain processes (e.g. genes highly expressed in sensory-motor regions) has considerable potential to parse out the contribution of specific pathways to alterations of brain architecture (Figure 5).

Figure 5.

Integrating top-down and bottom-up strategies in neuroimaging genomics. We propose a systematic investigation of a broad spectrum of neuropsychiatric variants to identify dimensions underlying idiopathic conditions. Multiscale and multimodal studies using multivariate approaches would allow for the identification of latent brain dimensions that best explain the relationships between genomic variants, biological processes, psychiatric conditions, and cognitive traits. Neuroimaging proxies of specific biological processes are identified through bottom-up approaches using individuals who carry mutations in genes involved in defined gene sets (akin to a polygenic score). Computing polygenic scores informed by biological and brain processes (e.g. genes highly expressed in sensory-motor regions) has considerable potential to parse out the contribution of specific pathways to alterations of brain architecture. Multivariate approaches such as canonical correlation analysis or structural equation modelling142 will allow investigating the relationship between genomic variants, neuroimaging features, psychiatric conditions, and behavioural traits. BIP = bipolar disorder; CCA = canonical correlation analysis; GE = gene expression components143; IQ = intelligence quotient; pLI = probability of being loss-of-function intolerant; PLS = partial least square regression; PRS-SZ = polygenic risk score for schizophrenia; Pvalb = parvalbumin143,144; SEM = structural equation modelling; SZ = schizophrenia. See also Table 1.

Larger GWAS studies will improve the amount of variance explained by polygenic risk scores and will increase the relevance of bottom-up neuroimaging genetics studies using common variants.[137–139] Such scores can capture individual-level variation and will be particularly appropriate for future predictive models.[145]

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