Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability

et al. 2021

Biological Psychiatry

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Section: Perspective and Future Directionsmentioning

confidence: 99%

Lessons Learned From Neuroimaging Studies of Copy Number Variants: A Systematic Review

Modenato

Martin‐Brevet

et al. 2021

Biological Psychiatry

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“…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 ( 10, 11 ). 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( 24, 25 ) or SZ ( 26, 27 ) ( r =0.76, p =0.02).…”

Section: Resultsmentioning

confidence: 99%

“…The latter has been modeled for SNPs, using polygenic scores (PGS), which are weighted sums of genetic risk alleles ( 9 ). 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 ( 10, 11 ).…”

Section: Introductionmentioning

confidence: 99%

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

Kumar

Harvey

et al. 2021

Preprint

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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.

“…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.…”

Section: Bottom-up Approach: Large Effect Genetic Variants To Dissect Mechanisms In Psychiatrymentioning

confidence: 99%

Dissecting autism and schizophrenia through neuroimaging genomics

Raznahan

Bellec

et al. 2021

Brain

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Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a ‘top-down’ approach, starting with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy starts at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: Why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In the review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioral and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modeling for diagnosis and clinical outcomes.