Autism and Schizophrenia-Associated CYFIP1 Regulates the Balance of Synaptic Excitation and Inhibition

Davenport, Elizabeth C.; Szulc, Blanka R.; Drew, James; Taylor, James O.; Morgan, Toby; Higgs, Nathalie; López‐Doménech, Guillermo; Kittler, Josef T.

doi:10.1016/j.celrep.2019.01.092

Cited by 102 publications

(100 citation statements)

References 89 publications

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“…LC-MS/MS further confirmed the veracity of our purification strategy as these high molecular weight species contained GABABRs, which has been previously shown to be associated with KCC2 (35). Interestingly we identified gephyrin associated with KCC2 in the 800 kDa band along with CYFIP1/2, NCKAP1 and CNTN1 and other proteins that are enriched at inhibitory synapses (51,52). NLGN3 was also detected, which can be present at both inhibitory and excitatory synapses (53).…”

Section: Discussionsupporting

confidence: 79%

The K-Cl co-transporter 2 is a point of convergence for multiple autism spectrum disorder and epilepsy risk gene products

Smalley

Kontou

Choi

et al. 2020

Preprint

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KCC2 plays a critical role in determining the efficacy of synaptic inhibition and deficits in its activity lead to epilepsy and neurodevelopmental delay. Here we use unbiased proteomic analyses to demonstrate that KCC2 forms stable protein complexes in the neuronal plasma membrane with 96 autism and/or epilepsy risk gene (ASD/Epi) products including ANKB, ANKG, CNTN1, ITPR1, NCKAP1, SCN2A, SHANK3, SPTAN1, and SPTBN1. Many of these proteins are also targets of Fragile-X mental retardation protein (FMRP), the inactivation of which is the leading monogenic cause of autism. Accordingly, the expression of a subset of these KCC2-binding partners was decreased in Fmr1 knockout mice. Fmr1 knockout compromised KCC2 phosphorylation, a key regulatory mechanism for transporter activity and the postnatal development of GABAergic inhibition. Thus, KCC2 is a point of convergence for multiple ASD/Epi risk genes and therapies targeting this transporter may have broad utility in alleviating these heterogeneous disorders and their associated epilepsies.

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Section: Discussionsupporting

confidence: 79%

The K-Cl co-transporter 2 is a point of convergence for multiple autism spectrum disorder and epilepsy risk gene products

Smalley

Kontou

Choi

et al. 2020

Preprint

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“…Posterior: Bayesian posterior probability of being a risk gene.We highlight the result of our top gene CYFIP1 to better understand how it was found by MIRAGE 259 (posterior probability of 0.86), but not Burden or SKAT-O (p value > 0.8 for both). This gene has high 260 posterior probability of being a risk gene (0.85), and is supported by multiple line of evidence from 261 literature, including association (copy number) with Autism, Schizophrenia and Intellectual Disability, 262 and its role in regulating synaptic activity and in mediating the function of FMR1, a well-known risk 263 gene of a syndromic form of Autism[39][40][41]. The signal of CYFIP1 is largely driven by a single very rare 264 (AF < 0.1%) damaging missense variant that occurs 8 times in cases but 0 in controls (Figure 6).…”

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confidence: 89%

MIRAGE: a Bayesian rare variant association analysis method incorporating functional information of variants

Han

Knoblauch²,

Wang³

et al. 2019

Preprint

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Rare genetic variants make significant contributions to human diseases. Compared to common variants, rare variants have larger effect sizes and are generally free of linkage disequilibrium (LD), which makes it easier to identify causal variants. Numerous methods have been developed to analyze rare variants in a gene or region in association studies, with the goal of finding risk genes by aggregating information of all variants of a gene. These methods, however, often make unrealistic assumptions, e.g. all rare variants in a risk gene would have non-zero effects. In practice, current methods for gene-based analysis often fail to show any advantage over simple single-variant analysis. In this work, we develop a Bayesian method: MIxture model based Rare variant Analysis on GEnes (MIRAGE). MIRAGE captures the heterogeneity of variant effects by treating all variants of a gene as a mixture of risk and non-risk variants, and models the prior probabilities of being risk variants as function of external information of variants, such as allele frequencies and predicted deleterious effects. MIRAGE uses an empirical Bayes approach to estimate these prior probabilities by combining information across genes. We demonstrate in both simulations and analysis of an exome-sequencing dataset of Autism, that MIRAGE significantly outperforms current methods for rare variant analysis. In particular, the top genes identified by MIRAGE are highly enriched with known or plausible Autism risk genes. Our results highlight several novel Autism genes with high Bayesian posterior probabilities and functional connections with Autism. MIRAGE is available at https://xinhe-lab.github.io/mirage. Introduction 1Genome-wide association studies (GWAS) have successfully identified thousands of loci associated with 2 human complex traits [1][2][3]. However, in most of these loci, the causal variants and their target genes 3 remain unknown. Additionally, most common variants (with minor allele frequency greater than 5%) 4 discovered by GWAS have small effect sizes, modifying disease risk by less than two fold [2,3]. 5Sequencing studies focusing on rare variants have the potential to improve our understanding of complex 6 diseases beyond GWAS. Because of purifying selection, deleterious variants with large effects on disease 7 risks tend to be rare in the population, as seen in the cases of many Mendelian diseases [4][5][6][7].Furthermore, linkage disequilibrium is much weaker for rare variants, making it less complicated to 9 fine-map causal variants. Exome sequencing studies have particular advantages because of their 10 relatively low costs, and the ability to directly implicate risk genes [8]. 11Statistical association tests for individual rare variants are usually under-powered due to their low allele 12 frequency. This poses a significant challenge for rare variant analysis. A natural strategy is to aggregate 13 all rare variants in a genomic region or gene, to test the collective association of the region or gene with 14 phenotype [9]. Over the pas...

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“…The site near the ST6GALNAC1 gene is hypomethylated in 7% of the cases, and is known to be associated (through hypomethylation) with schizophrenia and bipolar disorder in an identical twin methylation study who are discordant for these diseases [15]. CYFIP1 , here hypermethylated in 8% of the cases, was previously associated with schizophrenia and Autism through CNVs and is known to regulate the balance between synaptic excitation and inhibition [14]. The ABCA8 gene is important for lipid metabolism in oligodendrocytes, myelin formation and maintenance, and ABCA13 from the same subfamily is associated to schizophrenia through GWAS [29].…”

Section: Resultsmentioning

confidence: 99%

Finding associations in a heterogeneous setting: Statistical test for aberration enrichment

Mezlini

Das

Goldenberg

2020

Preprint

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Most two-group statistical tests are implicitly looking for a broad pattern such as an overall shift in mean, median or variance between the two groups. Therefore, they operate best in settings where the effect of interest is uniformly affecting everyone in one group versus the other. In real-world applications, there are many scenarios where the effect of interest is heterogeneous. For example, a drug that works very well on only a proportion of patients and is equivalent to a placebo on the remaining patients, or a disease associated gene expression dysregulation that only occurs in a proportion of cases whereas the remaining cases have expression levels indistinguishable from the controls for the considered gene. In these examples with heterogeneous effect, we believe that using classical two-group statistical tests may not be the most powerful way to detect the signal. In this paper, we developed a statistical test targeting heterogeneous effects and demonstrated its power in a controlled simulation setting compared to existing methods. We focused on the problem of finding meaningful associations in complex genetic diseases using omics data such as gene expression, miRNA expression, and DNA methylation. In simulated and real data, we showed that our test is complementary to the traditionally used statistical tests and is able to detect disease-relevant genes with heterogeneous effects which would not be detectable with previous approaches.

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Autism and Schizophrenia-Associated CYFIP1 Regulates the Balance of Synaptic Excitation and Inhibition

Cited by 102 publications

References 89 publications

The K-Cl co-transporter 2 is a point of convergence for multiple autism spectrum disorder and epilepsy risk gene products

The K-Cl co-transporter 2 is a point of convergence for multiple autism spectrum disorder and epilepsy risk gene products

MIRAGE: a Bayesian rare variant association analysis method incorporating functional information of variants

Finding associations in a heterogeneous setting: Statistical test for aberration enrichment

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