Biology subjects

Buxbaum, J.

Publications and source records attributed to Buxbaum, J..

13 recordsLinked to original sources

bioRxiv2026

Age-Related Increases in 40Hz Neural Synchrony Are Specific to Typical Development: A Cross-Sectional Study of Autism Spectrum Disorder

Background: The 40Hz auditory steady-state response (ASSR) is a measure of gamma-band neural synchrony sensitive to excitation-inhibition (E/I) balance. Disruptions to E/I balance have been implicated in autism spectrum disorder (ASD), making ASSR an efficient tool for investigating neural synchrony development in this population. Whether age-related differences in 40Hz ASSR are detectable across development in ASD remains understudied. Phelan-McDermid syndrome (PMS), a rare genetic disorder with a phenotype overlapping with autism, caused by SHANK3 disruption, provides a genetically defined model for further investigating E/I-related neural synchrony disruptions. Methods: We examined 40Hz inter-trial phase coherence (ITPC) as an index of neural synchrony across a wide age range (2-37 years) in 127 participants from four groups: TD (n=43), ASD without intellectual disability (w/o ID; n=37), ASD with intellectual disability (w/ID; n=24), and PMS (n=23). Given the distinct age and cognitive profiles of ASD subgroups in this sample, analyses were conducted in separate models: TD vs. ASD w/o ID across all ages, and TD vs. ASD w/ID vs. PMS restricted to participants under 18. Results: For the first time in a cross-sectional sample spanning a large age range, we show that 40Hz ITPC increases significantly with age in TD individuals, while this developmental trajectory is absent in ASD without intellectual disability. Among children and adolescents under 18, 40Hz ITPC did not differ across TD, ASD w/ID, and PMS, and IQ did not predict ITPC in clinical groups. A post-hoc analysis revealed higher ITPC in TD males than females, with no sex differences in ASD or PMS. Conclusions: We demonstrate that gamma-band ITPC trajectories diverge between TD and ASD, specifically in adulthood, with no such difference detectable in childhood. No significant group differences were found among TD, ASD w/ID, and PMS individuals under 18. These findings highlight the importance of age as a critical variable when measuring ASSR, and underscore the need for lifespan studies, particularly in genetically defined conditions such as PMS, to determine whether similar divergence emerges in adulthood.

neuroscience↗

bioRxiv2025

A framework to infer de novo exonic variants when parental genotypes are missing enhances association studies of autism

MotivationGene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these de novo variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer de novo status and use this inference for association studies. ResultsWe use data from autism spectrum disorder to illustrate and evaluate methods. Separating de novo from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a de novo score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate. Availability and ImplementationThe implementation code and publicly available data are provided at: https://github.com/HaeunM/TADA-RD.

bioinformatics↗

bioRxiv2024

A scalable, high-throughput neural development platform identifies shared impact of ASD genes on cell fate and differentiation

BackgroundDeleterious mutations in hundreds of genes confer high risk for neurodevelopmental disorders (NDDs), posing significant challenges for therapeutic development. Identifying convergent pathways shared across NDD genes could reveal high-impact therapeutic targets. MethodsTo identity convergent pathways in NDD genes, we optimized Perturb-seq, a method combining CRISPR perturbation with single-cell RNA sequencing (scRNA-seq), and applied structural topic modeling (STM) to simultaneously assess impact on cell fate and developmental stage. We then studied a subset of autism spectrum disorder (ASD) genes implicated in regulation of gene expression using these improved molecular and analytical approaches. ResultsResults from targeting 60 high-confidence ASD risk genes revealed significant effects on neural development. As expected, ASD risk genes impacted both progenitor fate and/or neuronal differentiation. Using STM, we could identify latent topics jointly capturing cell types, cell fate, and differentiation stages. Repression of ASD risk genes led to changes in topic proportions and effects of four genes (DEAF1, KMT2A, MED13L, and MYT1L) were validated in an independent dataset. ConclusionsOur optimized Perturb-seq method, combined with a novel analytical approach, provides a powerful, cost-effective framework for uncovering convergent mechanisms among genes involved in complex neurodevelopmental processes. Application of these methods advanced understanding of the impact of ASD mutations on multiple dimensions of neural development, and provides a framework for a broader examination of the function of NDD risk genes.

genomics↗

bioRxiv2024

Blended Genome Exome (BGE) as a Cost Efficient Alternative to Deep Whole Genomes or Arrays

Genomic scientists have long been promised cheaper DNA sequencing, but deep whole genomes are still costly, especially when considered for large cohorts in population-level studies. More affordable options include microarrays + imputation, whole exome sequencing (WES), or low-pass whole genome sequencing (WGS) + imputation. WES + array + imputation has recently been shown to yield 99% of association signals detected by WGS. However, a method free from ascertainment biases of arrays or the need for merging different data types that still benefits from deeper exome coverage to enhance novel coding variant detection does not exist. We developed a new, combined, "Blended Genome Exome" (BGE) in which a whole genome library is generated, an aliquot of that genome is amplified by PCR, the exome regions are selected and enriched, and the genome and exome libraries are combined back into a single tube for sequencing (33% exome, 67% genome). This creates a single CRAM with a low-coverage whole genome (2-3x) combined with a higher coverage exome (30-40x). This BGE can be used for imputing common variants throughout the genome as well as for calling rare coding variants. We tested this new method and observed >99% r2 concordance between imputed BGE data and existing 30x WGS data for exome and genome variants. BGE can serve as a useful and cost-efficient alternative sequencing product for genomic researchers, requiring ten-fold less sequencing compared to 30x WGS without the need for complicated harmonization of array and sequencing data.

genomics↗

bioRxiv2022

Chemico-genetic Analysis of Native Autism Proteomes Reveals Shared Biology Predictive of Functional Modifiers

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving high specificity spatial interactomes compared to prior methods. The resulting native proximity interactomes are enriched for human genes dysregulated in the brain of autistic individuals and reveal unexpected and highly significant interactions with other lower-confidence autism risk gene products, positing new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

neuroscience↗

bioRxiv2020

Effects of gene by microbiome interactions on behavioral and neurobiological phenotypes in a mouse model for autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder with complex pathophysiology including both genetic and environmental factors. Recent evidence demonstrates the gut microbiome and its resultant metabolome can influence brain and behavior and have been implicated in ASD. To investigate gene by microbiome interactions in a model for genetic risk of ASD, we utilize mutant mice carrying a deletion of the ASD-associated Shank3 gene (Shank3KO). Shank3KO have altered microbiome composition and function at baseline in addition to social deficits. Further depletion of the microbiome with antibiotics exacerbates social deficits in Shank3KO, and results in transcriptional changes in the frontal cortex. Supplementation with the microbial metabolite acetate leads to reversal of social behavioral phenotypes even in mice with a depleted microbiome, and significantly alters transcriptional regulation in the prefrontal cortex. These results suggest a key role for the gut microbiome and the neuroactive metabolite acetate in regulating ASD-like behaviors.

neuroscience↗

bioRxiv2019

Transcriptional signatures of participant-derived neural progenitor cells and neurons implicate altered Wnt signaling in Phelan McDermid syndrome and autism

BackgroundPhelan-McDermid syndrome (PMS) is a rare genetic disorder with high risk of autism spectrum disorder (ASD), intellectual disability and language delay, and is caused by 22q13.3 deletions or mutations in the SHANK3 gene. To date, the molecular and pathway changes resulting from SHANK3 haploinsufficiency in PMS remain poorly understood. Uncovering these mechanisms is critical for understanding pathobiology of PMS and, ultimately, for the development of new therapeutic interventions. MethodsWe developed human induced pluripotent stem cell (hiPSC)-based models of PMS by reprogramming peripheral blood samples from individuals with PMS (n=7) and their unaffected siblings (n=6). For each participant, up to three hiPSC clones were generated and differentiated into induced neural progenitor cells (iNPCs; n=32) and induced forebrain neurons (iNeurons; n=42). Genome-wide RNA-sequencing was applied to explore transcriptional differences between PMS probands and unaffected siblings. ResultsTranscriptome analyses identified 391 differentially expressed genes (DEGs) in iNPCs and 82 DEGs in iNeurons, when comparing cells from PMS probands and unaffected siblings (FDR <5%). Genes under-expressed in PMS were implicated in Wnt signaling, embryonic development and protein translation, while over-expressed genes were enriched for pre- and post-synaptic density genes, regulation of synaptic plasticity, and G-protein-gated potassium channel activity. Gene co-expression network analysis identified two modules in iNeurons that were over-expressed in PMS, implicating postsynaptic signaling and GDP binding, and both modules harbored a significant enrichment of genetic risk loci for developmental delay and intellectual disability. Finally, PMS-associated genes were integrated with other ASD iPSC transcriptome findings and several points of convergence were identified, indicating altered Wnt signaling, extracellular matrix and glutamatergic synapses. LimitationsGiven the rarity of the condition, we could not carry out experimental validation in independent biological samples. In addition, functional and morphological phenotypes caused by loss of SHANK3 were not characterized here. ConclusionsThis is the largest human neural sample analyzed in PMS. Genome-wide RNA-sequencing in hiPSC-derived neural cells from individuals with PMS revealed both shared and distinct transcriptional signatures across iNPCs and iNeurons, including many genes implicated in risk for ASD, as well as specific neurobiological pathways, including the Wnt pathway.

neuroscience↗

bioRxiv2019

Whole-genome and RNA sequencing reveal variation and transcriptomic coordination in the developing human prefrontal cortex

Variation in gene expression underlies neurotypical development, while genomic variants contribute to neuropsychiatric disorders. BrainVar is a unique resource of paired whole-genome sequencing and bulk-tissue RNA-sequencing from the human dorsolateral prefrontal cortex of 176 neurotypical individuals across prenatal and postnatal development, providing the opportunity to assay genomic and transcriptomic variation in tandem. Leveraging this resource, we identified rare premature stop codons with commensurate reduced and allele-specific expression of corresponding genes, and common variants that alter gene expression (expression quantitative trait loci, eQTLs). Categorizing eQTLs by prenatal and postnatal effect, genes affected by temporally-specific eQTLs, compared to constitutive eQTLs, are enriched for haploinsufficiency, protein-protein interactions, and neuropsychiatric disorder risk loci. Expression levels of over 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell type specific genes and neuropsychiatric disorder loci, underscoring the importance of cataloguing developmental trajectories in understanding cortical physiology and pathology.\n\nHighlightsO_LIWhole-genome and RNA-sequencing across human prefrontal cortex development in BrainVar\nC_LIO_LIGene-specific developmental trajectories characterize the late-fetal transition\nC_LIO_LIIdentification of constitutive, prenatal-specific, postnatal-specific, and rare eQTLs\nC_LIO_LIIntegrated analysis reveals genetic and developmental influences on CNS traits and disorders\nC_LI

neuroscience↗

bioRxiv2018

Prioritizing risk genes for neurodevelopmental disorders using pathway information

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.

genomics↗

bioRxiv2018

mTADA: a framework for analyzing de novo mutations in multiple traits

Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. In addition, approaches that consider multiple traits can aid in the characterization of shared genetic etiology among those traits. In recent years, parent-offspring trio studies have reported an enrichment of de novo mutations (DNMs) in neuropsychiatric disorders. The analysis of DNM data in the context of neuropsychiatric disorders has implicated multiple putatively causal genes, and a number of reported genes are shared across disorders. However, a joint analysis method designed to integrate de novo mutation data from multiple studies has yet to be implemented. We here introduce multi pi e-trait TAD A (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. mTADA uses two single-trait analysis data sets to estimate the proportion of overlapping risk genes, and reports genes shared between and specific to the relevant disorders. We applied mTADA to >13,000 trios for six disorders: schizophrenia (SCZ), autism spectrum disorder (ASD), developmental disorders (DD), intellectual disability (ID), epilepsy (EPI), and congenital heart disease (CHD). We report the proportion of overlapping risk genes and the specific risk genes shared for each pair of disorders. A total of 153 genes were found to be shared in at least one pair of disorders. The largest percentages of shared risk genes were observed for pairs of DD, ID, ASD, and CHD (>20%) whereas SCZ, CHD, and EPI did not show strong overlaps In risk gene set between them. Furthermore, mTADA identified additional SCZ, EPI and CHD risk genes through integration with DD de novo mutation data. For CHD, using DD information, 31 risk genes with posterior probabilities > 0.8 were identified, and 20 of these 31 genes were not in the list of known CHD genes. We find evidence that most significant CHD risk genes are strongly expressed in prenatal stages of the human genes. Finally, we validated our findings for CHD and EPI in independent cohorts comprising 1241 CHD trios, 226 CHD singletons and 197 EPI trios. Multiple novel risk genes identified by mTADA also had de novo mutations in these independent data sets. The joint analysis method introduced here, mTADA, is able to identify risk genes shared by two traits as well as additional risk genes not found through single-trait analysis only. A number of risk genes reported by mTADA are identified only through joint analysis, specifically when ASD, DD, or ID are one of the two traits examined. This suggests that novel genes for the trait or a new trait might converge to a core gene list of the three traits.

genomics↗

bioRxiv2017

Elevated polygenic burden for autism is associated with differential DNA methylation at birth.

BackgroundAutism spectrum disorder (ASD) is a severe neurodevelopmental disorder characterized by deficits in social communication and restricted, repetitive behaviors, interests, or activities. The etiology of ASD involves both inherited and environmental risk factors, with epigenetic processes hypothesized as one mechanism by which both genetic and non-genetic variation influence gene regulation and pathogenesis.\n\nMethodsWe quantified neonatal methylomic variation in 1,263 infants - of whom ~50% went on to subsequently develop ASD - using DNA isolated from a unique collection of archived blood spots taken shortly after birth. We used matched genetic data from the same individuals to examine the molecular consequences of ASD genetic risk variants, identifying methylomic variation associated with elevated polygenic burden for ASD. In addition, we performed DNA methylation quantitative trait loci (mQTL) mapping to prioritize target genes from ASD GWAS findings.\n\nResultsAlthough we did not identify specific loci showing consistent changes in neonatal DNA methylation associated with later ASD, we found a significant association between increased polygenic burden for autism and methylomic variation at two CpG sites located proximal to a robust GWAS signal for ASD on chromosome 8.\n\nConclusionsThis study is the largest analysis of DNA methylation in ASD yet undertaken and the first to integrate both genetic and epigenetic variation at birth in ASD. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with disease, and of using mQTL to refine the functional and regulatory variation associated with ASD risk variants.

genetics↗

bioRxiv2017

Bayesian Integrated Analysis Of Multiple Types Of Rare Variants To Infer Risk Genes For Schizophrenia And Other Neurodevelopmental Disorders

BackgroundIntegrating rare variation from trio family and case/control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DD), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through study of rare variation, only two risk genes have been identified.\n\nMethodsWe used hierarchical Bayesian modeling of rare variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence (WES) data for schizophrenia (1,077 trios, 6,699 cases and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).\n\nResultsFor SCZ, we estimate 1,551 risk genes, more risk genes and weaker effects than for NDDs. We provide power analyses to predict the number of risk gene discoveries as more data become available, demonstrating greater value of case-control over trio samples. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs ({rho} > 0.55), but low between SCZ and the NDDs ({rho} < 0.3). In depth analysis of 288 NDD genes shows highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichments, single-cell RNA-seq (scRNAseq) cell types and multi-region developmental brain RNA-seq.\n\nConclusionsWe have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs. We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

genomics↗

bioRxiv2017

New mutations, old statistical challenges

Based on targeted sequencing of 208 genes in 11,730 neurodevelopmental disorder cases, Stessman et al. report the identification of 91 genes associated (at a False Discovery Rate [FDR] of 0.1) with autism spectrum disorders (ASD), intellectual disability (ID), and developmental delay (DD)--including what they characterize as 38 novel genes, not previously reported as connected with these diseases1.\n\nIf true, this would represent a substantial step forward. Unfortunately, each of the two discovery analyses (1. De novo mutation analysis and, 2. a comparison of private mutations with public control data) contain critical statistical flaws. When one accounts for these problems, fewer than half of the genes--and very few, if any, of the novel findings--survive. These errors have implications for how future analyses should be conducted, for understanding the genetic basis of these disorders, and for genomic medicine.\n\nWe discuss the two main ana ...

genetics↗