Transcriptional and functional consequences of alterations to MEF2C and its topological organization in neuronal models

Mohajeri, Kiana; Yadav, Rachita; D’haene, Eva; Boone, Philip M.; Erdin, Serkan; Gao, Dadi; Moysés-Oliveira, Mariana; Bhavsar, Riya; Currall, Benjamin; O’Keefe, Kathryn; Burt, Nicholas D.; Lowther, Chelsea; Lucente, Diane; Salani, Monica; Larson, Mathew; Redin, Claire; Dudchenko, Olga; Aiden, E; Menten, Björn; Tai, Derek J.C.; Gusella, James F.; Vergult, Sarah; Talkowski, Michael E.

doi:10.1016/j.ajhg.2022.09.015

Cited by 12 publications

(15 citation statements)

References 103 publications

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“…For SNVs, these included the identification of non-coding variants affecting the so-called gene body, where rare (de novo) variants affected, for instance, sequences of the UTRs disrupting the wild-type translation start site [96,97] or (de novo) variants in introns, where they introduced cryptic splice sites leading to abnormal splicing and the inclusion of poison exons [98][99][100]. For non-coding structural variants, the molecular mechanism leading to disease has mainly been the dysregulation of spatial 3D-organization [101] and gene regulation, for instance, through the disruption of topological-associated domain structures [102][103][104].…”

Section: From Exomes To Genomes-from Coding To Non-coding Variantsmentioning

confidence: 99%

The Genetics of Intellectual Disability

2023

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Intellectual disability (ID) has a prevalence of ~2–3% in the general population, having a large societal impact. The underlying cause of ID is largely of genetic origin; however, identifying this genetic cause has in the past often led to long diagnostic Odysseys. Over the past decades, improvements in genetic diagnostic technologies and strategies have led to these causes being more and more detectable: from cytogenetic analysis in 1959, we moved in the first decade of the 21st century from genomic microarrays with a diagnostic yield of ~20% to next-generation sequencing platforms with a yield of up to 60%. In this review, we discuss these various developments, as well as their associated challenges and implications for the field of ID, which highlight the revolutionizing shift in clinical practice from a phenotype-first into genotype-first approach.

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Section: From Exomes To Genomes-from Coding To Non-coding Variantsmentioning

confidence: 99%

The Genetics of Intellectual Disability

2023

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“…Several translocations involving different chromosomes distal to MEF2C have been reported in the literature. [104,[119][120][121] They all disrupt the MEF2C TAD 3D architecture creating a new MEF2C-regulatory landscape specific to each rearrangement. Nevertheless, all reported SVs cause a similar haploinsufficiency phenotype due to haploinsufficient MEF2C expression.…”

Section: Interpreting the Impact Of Svs: 3d And Morementioning

confidence: 99%

Disruption of regulatory domains and novel transcripts as disease‐causing mechanisms

Allou

Mundlos

2023

BioEssays

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Deletions, duplications, insertions, inversions, and translocations, collectively called structural variations (SVs), affect more base pairs of the genome than any other sequence variant. The recent technological advancements in genome sequencing have enabled the discovery of tens of thousands of SVs per human genome. These SVs primarily affect non‐coding DNA sequences, but the difficulties in interpreting their impact limit our understanding of human disease etiology. The functional annotation of non‐coding DNA sequences and methodologies to characterize their three‐dimensional (3D) organization in the nucleus have greatly expanded our understanding of the basic mechanisms underlying gene regulation, thereby improving the interpretation of SVs for their pathogenic impact. Here, we discuss the various mechanisms by which SVs can result in altered gene regulation and how these mechanisms can result in rare genetic disorders. Beyond changing gene expression, SVs can produce novel gene‐intergenic fusion transcripts at the SV breakpoints.

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“…Even in the case of uniform effect sizes across variant classes β = (1, 1, 1), meaning that the variant annotation was uninformative, COAST was more powerful than SKAT-O. This is likely due to its ability to integrate evidence across multiple models via (5). For the SKAT phenotype in Panel C, COAST was most powerful when the allelic series weights matched the true pattern of effect sizes, but SKAT-O tended to acquire a slight power advantage when the weights did not match.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…More recently, Duan et al [4] described a naturally occurring allelic series in the BHLHA9 locus associated with the clinical severity and penetrance of congenital limb malformations. Allelic series can also be generated experimentally, as was done recently by Mohajeri et al [5], who used CRISPR to engineer an allelic series of MEF2C mutations in vitro , providing a cellular model for human neurodevelopmental disorders. Motivated by these examples, we introduce a rare variant association test intended for identifying allelic series in large genotype-phenotype cohorts, such as the UK Biobank [6].…”

Section: Introductionmentioning

confidence: 99%

An allelic series rare variant association test for candidate gene discovery

McCaw

Kugathasan

Bereket

et al. 2022

Preprint

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Allelic series are of candidate therapeutic interest due to the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a gene in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and develop a gene-based rare variant association test specifically targeted for the identification of allelic series. Building on the well-known burden and sequence kernel association tests (SKAT), we specify a variety of association models, covering different genetic architectures, and integrate these into a COding-variant Allelic Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic series for 4 circulating lipid traits and 5 cell count traits among 145,735 subjects with available whole exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni significant associations with circulating lipid traits, on average, and 82% more with cell count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant results in the full cohort (Genebass, N=400K), or from common variant results in the GWAS catalog. In addition to detecting many gene-trait associations present in Genebass using only a fraction (36.9%) of the sample, COAST detects associations, such as ANGPTL4 with triglycerides, that are absent from Genebass but which have clear common variant support.

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Transcriptional and functional consequences of alterations to MEF2C and its topological organization in neuronal models

Cited by 12 publications

References 103 publications

The Genetics of Intellectual Disability

The Genetics of Intellectual Disability

Disruption of regulatory domains and novel transcripts as disease‐causing mechanisms

An allelic series rare variant association test for candidate gene discovery

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