Dissecting Autism and Schizophrenia Through Neuroimaging Genomics

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


Brain. 2021;144(7):1943-1957. 

In This Article

Conclusion: What Have we Learned and What are the Next Steps?

Early neuroimaging genomic studies in psychiatry were plagued by small sample sizes and inappropriate candidate gene strategies. Studies of psychiatric disorders were performed on the assumption of relative specificity (Box 1). With access to larger datasets in the past years, both top-down and bottom-up neuroimaging-genomics studies have gained traction with increased reproducibility of nature and effect-size of the alterations. The effect sizes of rare variants on neuroimaging endophenotypes are concordant with effects previously measured for the same variants on brain structure, cognitive and behavioural traits.[73,133] This is in striking contrast with the effect sizes observed for functional connectivity and brain structure in schizophrenia, ASD, and ADHD, which are 3–5-fold lower (Figure 2).[35,39]

This surprising discordance of effect-sizes observed between bottom-up (rare variants) and top-down studies (idiopathic conditions) underscore the necessity to dissect results from case-control studies conducted in idiopathic conditions with results from large-effect size rare variants. We propose a genetically-informed stratification by systematically investigating a broad spectrum of neuropsychiatric variants. This should allow for the identification of latent dimensions in idiopathic conditions.

The shared neuroimaging dimensions identified across psychiatric conditions are in line with the genetic correlation demonstrated between the same conditions as well as pleiotropic effects of genomic variants (Figure 1). Findings also suggest a staggering diversity of brain endophenotypes across different genomic variants and idiopathic psychiatric conditions. Therefore, the time has not yet arrived to draw firm conclusions about the nature of the potential neuroimaging convergence (or lack thereof), across genetic risk and psychiatric conditions.

The neuroimaging field is increasingly moving towards harmonization using systematic analytical methods, atlas, and data structure[58,153,154] as well as reporting standards including effect-sizes and un-thresholded beta map (Poldrack Nature). Large efforts have been in building platforms to associate imaging modalities and genetic data.[155–158]

Only a few datasets currently allow neuroimaging genomic studies (Figure 6): UKBB[161] and ABCD[173] are large population cohorts with great potential to study genomic variation and neuroimaging phenotypes, but they include few individuals with mental illnesses and behavioural deficits. EU-AIMS is among the few psychiatric cohorts integrating genomics, neuroimaging and cognitive data, in ~250 individuals with autism.[172] Given our assumptions on the mechanistic heterogeneity in ASD, one would expect that a neuroimaging genomic dataset of several thousand individuals with autism would be required to provide the power to investigate brain-molecular dimensions. Of note, there are currently no neuroimaging genomic cohorts in schizophrenia that are available to the community. The ENIGMA consortium[160] has also been instrumental in moving the field and has provided well-powered meta-analytic studies.

Figure 6.

Historical timeline in neuroimaging genetics. Many advances in neuroimaging genomics have been made by large-scale initiatives and cohort studies, such as the Autism Brain Imaging Data Exchange (ABIDE),55 the Psychiatric Genomics Consortium (PGC),159 the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium160 and the UK Biobank.161 These collaborative initiatives, among others, facilitate advances in psychiatry by providing large brain imaging and genomics datasets to the research community worldwide. Human Genome Project (HGP)162; Neuroimaging Tools and Resources Collaboratory (NITRC platform); Psychiatric Genomics Consortium (PGC)159; Alzheimer's Disease Neuroimaging Initiative (ADNI)163; 1000 Genomes164; ADHD-200165; Open fMRI166; Human Brain Project (HBP)167; ENIGMA Consortium = Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA Consortium)160; = Autism Brain Imaging Data Exchange (ABIDE-1)55; NeuroVault168; UK Biobank161; HCP = Human Connectome Project (HCP)169; PING = Pediatric Imaging; Neurocognition; and Genetics (PING)170; SchizConnect171; EU-Aims172; Adolescent Brain Cognitive Development (ABCD).173

Neuroimaging genetic studies investigating large effect size mutations are lagging behind those focused on common variation. Closing this gap will require investing in new large scale cohorts with exome/genome sequencing data collected in individuals with a broad spectrum of psychiatric conditions. Cohorts with such data include UKBB and EU-AIMS. Alternative strategies include gene cohorts ascertaining individuals with previously identified large effect size neuropsychiatric variants such as ENIGMA-CNV, ENIGMA 22q11.2, and Quebec 1000 families. These efforts should provide significant power to associate brain mechanisms to genomic variants, molecular mechanisms, and mental illnesses. They will likely improve predictive modelling at the individual level and guide the development of mechanistically informed predictive tests with clinical utility.