Biology subjects

Sebat, J.

Publications and source records attributed to Sebat, J..

15 recordsLinked to original sources

bioRxiv2025

Mirror effect of genomic deletions and duplications on cognitive ability across the human cerebral cortex

Cognitive deficits are common across many neurodevelopmental and psychiatric conditions, including those studied in the current set of PGC-CNV papers. How changes in regional gene expression across the cerebral cortex influence cognitive ability remains unknown. Population variation in gene dosage--which significantly impacts gene expression--represents a unique paradigm to address this question. We developed a cerebral-cortex gene-set burden analysis (CC-GSBA) to associate a trait with genomic deletions and duplications that disrupt genes with similar expression profiles across 180 cortical regions. We performed CC-GSBA across 180 cortical regions to test associations with cognitive ability in 260,000 individuals from general population cohorts. Most cortical gene sets were associated with a decrease in cognitive ability when deleted or duplicated, and this novel approach revealed opposing cortical patterns for the effect sizes of deletions and duplications. These cortical patterns of effect sizes followed the cortical gradient previously characterized at the molecular, cellular, and functional levels. We show that genes with preferential expression in sensorimotor regions demonstrated the largest effect on cognition when deleted. At the opposing end of the cortical gradient, genes with preferential expression in multimodal association regions affected cognition the most when duplicated. These two gene dosage cortical patterns could not be explained by particular cell types, developmental epochs, or genetic constraints, highlighting the fact that the macroscopic network organization of the cerebral cortex is key to understanding the effects of gene dosage on cognitive traits.

neuroscience↗

bioRxiv2024

Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes

Genomic Copy Number Variants (CNVs) that increase risk for neurodevelopmental disorders are also associated with lower cognitive ability in general population cohorts. Studies have focussed on a small set of recurrent CNVs, but burden analyses suggested that the vast majority of CNVs affecting cognitive ability are too rare to reach variant-level association. As a result, the full range of gene-dosage-sensitive biological processes linked to cognitive ability remains unknown. To investigate this issue, we identified all CNVs >50 kilobases in 258k individuals from 6 general population cohorts with assessments of general cognitive abilities. We performed a CNV-GWAS and functional burden analyses, which tested 6502 gene-sets defined by tissue and cell-type transcriptomics as well as gene ontology disrupted by all rare coding CNVs. CNV-GWAS identified a novel duplication at 2q12.3 associated with higher performance in cognitive ability. Among the 864 gene-sets associated with cognitive ability, only 11% showed significant effects for both deletions and duplication. Accordingly, we systematically observed negative correlations between deletion and duplication effect sizes across all levels of biological observations. We quantified the preferential effects of deletions versus duplication using tagDS, a new normalized metric. Cognitive ability was preferentially affected by cortical, presynaptic, and negative-regulation gene-sets when duplicated. In contrast, preferential effects of deletions were observed for subcortical, post-synaptic, and positive-regulation gene-sets. A large proportion of gene-sets assigned to non-brain organs were associated with cognitive ability due to low tissue specificity genes, which were associated with higher sensitive to haploinsufficiency. Overall, most biological functions associated with cognitive ability are divided into those sensitive to either deletion or duplications.

genetics↗

bioRxiv2023

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

The seventh iteration of the reference genome assembly for Rattus norvegicus-- mRatBN7.2--corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared to its predecessor. Gene annotations are now more complete, significantly improving the mapping precision of genomic, transcriptomic, and proteomics data sets. We jointly analyzed 163 short-read whole genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined [~]20.0 million sequence variations, of which 18.7 thousand are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

genomics↗

bioRxiv2021

Customized de novo mutation detection for any variant calling pipeline: SynthDNM

MotivationAs sequencing technologies and analysis pipelines evolve, DNM calling tools must be adapted. Therefore, a flexible approach is needed that can accurately identify de novo mutations from genome or exome sequences from a variety of datasets and variant calling pipelines. ResultsHere, we describe SynthDNM, a random-forest based classifier that can be readily adapted to new sequencing or variant-calling pipelines by applying a flexible approach to constructing simulated training examples from real data. The optimized SynthDNM classifiers predict de novo SNPs and indels with robust accuracy across multiple methods of variant calling. AvailabilitySynthDNM is freely available on Github (https://github.com/james-guevara/synthdnm) Contactjsebat@ucsd.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

DeepMosaic: Control-independent mosaic single nucleotide variant detection using deep convolutional neural networks

Introductory paragraphMosaic variants (MVs) reflect mutagenic processes during embryonic development1 and environmental exposure2, accumulate with aging, and underlie diseases such as cancer and autism3. The detection of MVs has been computationally challenging due to sparse representation in non-clonally expanded tissues. While heuristic filters and tools trained on clonally expanded MVs with high allelic fractions are proposed, they show relatively lower sensitivity and more false discoveries4-9. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs, and a convolutional neural networks-based classification module for control-independent MV detection. DeepMosaic achieved higher accuracy compared with existing methods on biological and simulated sequencing data, with a 96.34% (158/164) experimental validation rate. Of 932 mosaic variants detected by DeepMosaic in 16 whole genome sequenced samples, 21.89-58.58% (204/932-546/932) MVs were overlooked by other methods. Thus, DeepMosaic represents a highly accurate MV classifier that can be implemented as an alternative or complement to existing methods.

genomics↗

bioRxiv2020

Temporal stability of human sperm mosaic mutations results in life-long threat of transmission to offspring

Every newborn harbors scores of new single nucleotide variants (SNVs) that may impact health and disease1-4; the majority of these are contributed by the paternal germ cells5. In some cases, these mutations are identifiable in a subset of the parents cells--a phenomenon called mosaicism, which is capable of producing disease recurrence6-8. Here, we provide a comprehensive analysis of male gonadal mosaic mutations, employing 300x whole genome sequencing (WGS) of blood and sperm in 17 healthy individuals, including assessment across multiple samples and age groups. Approximately 1 in 15 healthy males is predicted to harbor a transmissible, likely pathogenic exonic variant that is mosaic in his sperm. In general, only a third of sperm mosaic mutations were detectable in blood cells, all were remarkably stable over the course of months to years, and 23% were present in 5% or more of sperm cells. There was no evidence of age-dependent clonal expansion or collapse, as seen in hematopoiesis. Thus, despite the observed increase of mutations in offspring of men with advanced paternal age, detectable sperm mosaicism remains stable, represents a life-long transmission risk to offspring, and suggests a testis stem cell niche that prevents widespread clonality.

genomics↗

bioRxiv2020

Cortical Organoids Model Early Brain Development Disrupted by 16p11.2 Copy Number Variants in Autism

Reciprocal deletion and duplication of 16p11.2 region is the most common copy number variation (CNV) associated with Autism Spectrum Disorders. We generated cortical organoids from skin fibroblasts of patients with 16p11.2 CNV to investigate impacted neurodevelopmental processes. We show that organoid size recapitulates macrocephaly and microcephaly phenotypes observed in the patients with 16p11.2 deletions and duplications. The CNV has mirror-opposite effect on neuronal maturation, proliferation, and synapse number, in concordance with its effect on brain growth in humans. We demonstrate that 16p11.2 CNV alters the ratio of neurons to neural progenitors in organoids during early neurogenesis, with excess of neurons and depletion of neural progenitors observed in deletions, and mirror phenotypes in duplications. Transcriptomic and proteomic profiling revealed multiple dysregulated pathways, including defects in neuron migration. Inhibition of activity of the small GTPase RhoA rescued migration deficits. This study provides insights into potential neurobiological mechanisms behind the 16p11.2 CNV during neocortical development.

neuroscience↗

bioRxiv2020

Importance of polymorphic SNPs, short tandem repeats and structural variants for differential gene expression among inbred C57BL/6 and C57BL/10 substrains

Mouse substrains are an invaluable model for understanding disease. We compared C57BL/6J, which is the most commonly used inbred mouse strain, with 8 C57BL/6 and 5 C57BL/10 closely related inbred substrains. Whole genome sequencing and RNA-sequencing analysis yielded 352,631 SNPs, 109,096 INDELs, 150,344 short tandem repeats (STRs), 3,425 structural variants (SVs) and 2,826 differentially expressed genes (DEGenes) among these 14 strains. 312,981 SNPs (89%) distinguished the B6 and B10 lineages. These SNPs were clustered into 28 short segments that are likely due to introgressed haplotypes rather than new mutations. Outside of these introgressed regions, we identified 53 SVs, protein-truncating SNPs and frameshifting INDELs that were associated with DEGenes. Our results can be used for both forward and reverse genetic approaches, and illustrate how introgression and mutational processes give rise to differences among these widely used inbred substrains.

genomics↗

bioRxiv2020

Autism-linked Cullin3 germline haploinsufficiency impacts cytoskeletal dynamics and cortical neurogenesis through RhoA signaling

E3-ubiquitin ligase Cullin3 (Cul3) is a high confidence risk gene for Autism Spectrum Disorder (ASD) and Developmental Delay (DD). To investigate how Cul3 mutations impact brain development, we generated haploinsufficient Cul3 mouse model using CRISPR/Cas9 genome engineering. Cul3 mutant mice exhibited social and cognitive deficits and hyperactive behavior. Brain MRI found decreased volume of cortical regions and changes in many other brain regions of Cul3 mutant mice starting from early postnatal development. Spatiotemporal transcriptomic and proteomic profiling of the brain implicated neurogenesis and cytoskeletal defects as key drivers of Cul3 functional impact. Specifically, dendritic growth, filamentous actin puncta, and spontaneous network activity were reduced in Cul3 mutant mice. Inhibition of small GTPase RhoA, a molecular substrate of Cul3 ligase, rescued dendrite length and network activity phenotypes. Our study identified neuronal cytoskeleton and Rho signaling as primary targets of Cul3 mutation during early brain development.

neuroscience↗

bioRxiv2019

Common DNA sequence variation influences 3-dimensional conformation of the human genome

The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. To address these questions, we performed Hi-C on Lymphoblastoid Cell Lines (LCLs) from 20 individuals. We identified thousands of regions across the genome where 3D chromatin conformation varies between individuals and found that this conformational variation is often accompanied by variation in gene expression, histone modifications, and transcription factor (TF) binding. Moreover, we found that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality and density of local cis contacts. We mapped hundreds of Quantitative Trait Loci (QTLs) associated with 3D chromatin features and found evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.

genomics↗

bioRxiv2019

Oligogenic effects of 16p11.2 copy number variation on craniofacial development

A copy number variant (CNV) of 16p11.2, which encompasses 30 genes, is associated with developmental and psychiatric disorders, head size and body mass. The genetic mechanisms that underlie these associations are not understood. To elucidate the effects of genes on development, we exploited the quantitative effects of CNV on craniofacial structure in humans and model organisms. We show that reciprocal deletion and duplication of 16p11.2 have characteristic "mirror" effects on craniofacial features that are conserved in human, rat and mouse. By testing gene dosage effects on the shape of the mandible in zebrafish, we show that the distribution of effects for all individual genes is consistent with that of the CNV, and some combinations have non-additive effects. Our results suggest that, at minimum, one third of genes within the 16p11.2 region influence craniofacial development, and the facial gestalt of each CNV represents a product of 30 dosage effects. HighlightsO_LIReciprocal CNVs of 16p11.2 have mirror effects on craniofacial structure. Copy number is associated with a positive effect on nasal and mandibular regions and a negative effect on frontal regions of the face. C_LIO_LIEffects of CNV on craniofacial development in human are well conserved in rat and mouse models of 16p11.2 deletion and duplication. C_LIO_LI7/30 genes each independently have significant effects on the shape of the mandible in zebrafish; these include SPN, C16orf54, SEZ6L2, ASPHD1, TAOK2, INO80E and FAM57B. Others (MAPK3, MVP, KCTD13) have detectable effects only in combination. C_LIO_LIOverexpression of 30 genes individually showed a distribution of effects that was skewed in the same direction as that of the full duplication, suggesting that specific facial features represent the net of all individual effects combined. C_LI

genetics↗

bioRxiv2017

Quantification of autism recurrence risk by direct assessment of paternal sperm mosaicism

De novo genetic mutations represent a major contributor to pediatric disease, including autism spectrum disorders (ASD), congenital heart disease, and muscular dystrophies1,2, but there are currently no methods to prevent or predict them. These mutations are classically thought to occur either at low levels in progenitor cells or at the time of fertilization1,3 and are often assigned a low risk of recurrence in siblings4,5. Here, we directly assess the presence of de novo mutations in paternal sperm and discover abundant, germline-restricted mosaicism. From a cohort of ASD cases, employing single molecule genotyping, we found that four out of 14 fathers were germline mosaic for a putatively causative mutation transmitted to the affected child. Three of these were enriched or exclusively present in sperm at high allelic fractions (AF; 7-15%); and one was recurrently transmitted to two additional affected children, representing clinically actionable information. Germline mosaicism was further assessed by deep (>90x) whole genome sequencing of four paternal sperm samples, which detected 12/355 transmitted de novo single nucleotide variants that were mosaic above 2% AF, and more than two dozen additional, non-transmitted mosaic variants in paternal sperm. Our results demonstrate that germline mosaicism is an underestimated phenomenon, which has important implications for clinical practice and in understanding the basis of human disease. Genetic analysis of sperm can assess individualized recurrence risk following the birth of a child with a de novo disease, as well as the risk in any male planning to have children.

genetics↗

bioRxiv2017

MutPred2: inferring the molecular and phenotypic impact of amino acid variants

We introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. While its prioritization performance is state-of-the-art, a novel and distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited diseases have the potential to significantly accelerate the discovery of clinically actionable variants.\n\nAvailability: http://mutpred.mutdb.org/

bioinformatics↗

bioRxiv2017

Paternally inherited noncoding structural variants contribute to autism

The genetic architecture of autism spectrum disorder (ASD) is known to consist of contributions from gene-disrupting de novo mutations and common variants of modest effect. We hypothesize that the unexplained heritability of ASD also includes rare inherited variants with intermediate effects. We investigated the genome-wide distribution and functional impact of structural variants (SVs) through whole genome analysis ([≥]30X coverage) of 3,169 subjects from 829 families affected by ASD. Genes that are intolerant to inactivating variants in the exome aggregation consortium (ExAC) were depleted for SVs in parents, specifically within fetal-brain promoters, UTRs and exons. Rare paternally-inherited SVs that disrupt promoters or UTRs were over-transmitted to probands (P = 0.0013) and not to their typically-developing siblings. Recurrent functional noncoding deletions implicate the gene LEO1 in ASD. Protein-coding SVs were also associated with ASD (P = 0.0025). Our results establish that rare inherited SVs predispose children to ASD, with differing contributions from each parent.

genomics↗

bioRxiv2017

SV2: Accurate Structural Variation Genotyping and De Novo Mutation Detection

MotivationStructural Variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease.\n\nResultsHere we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations.\n\nAvailability and ImplementationSV2 is freely available on GitHub (https://github.com/dantaki/SV2)

bioinformatics↗