Biology subjects

Talkowski, M.

Publications and source records attributed to Talkowski, M..

7 recordsLinked to original sources

bioRxiv2024

Polymorphic tandem repeats shape single-cell gene expression across the immune landscape

Tandem repeats (TRs) - highly polymorphic, repetitive sequences across the human genome - are important regulators of gene expression but remain underexplored due to challenges in accurate genotyping and analysis1. Here, we generate new whole genome and single-cell RNA sequencing from >5.4 million blood-derived cells across 1,925 individuals in two cohorts [Cuomo et al., accompanying manuscript], and perform meta-analysis to characterize the impact of variation in >1.7 million TR loci on immune cell type-specific gene expression. We identify >69,000 single-cell expression TR loci (sc-eTRs), 30.7% of which are specific to one of 28 immune cell types, and reveal dynamic regulatory effects using cell-state inference. Matched single-cell ATAC sequencing profiles from >3.4 million nuclei in 922 individuals [Xue et al., accompanying manuscript]. uncover chromatin accessibility QTLs for nearly one-third of expression-associated TRs, supporting coordinated effects on cis-regulatory architecture. Fine-mapping implicates 1,490 TRs as candidate causal drivers of gene expression in 6.1% of tested genes, and colocalization analyses highlight >200 genes in which TRs likely mediate genetic associations with immune and hematological traits. Together, these results provide a genome-wide, multiomic view of TR-mediated regulation in the human immune system, establishing TRs as key contributors to cell type-specific regulatory variation and complex trait architecture.

genomics↗

bioRxiv2022

Genome-wide analysis of Structural Variants in Parkinson's Disease using Short-Read Sequencing data

Parkinsons disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinsons disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinsons disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehensive structural variant confirmation. We detected, genotyped, and tested 3,154 "high-confidence" common structural variant loci, representing over 412 million nucleotides of non-reference genetic variation. Using the long-read sequencing data, we validated three structural variants that may drive the association signals at known Parkinsons disease risk loci, including a 2kb intronic deletion within the gene LRRN4. Further, we confirm that the majority of structural variants in the human genome cannot be detected using short-read sequencing alone, encompassing on average around 4 million nucleotides of inaccessible sequence per genome. Therefore, although these data provide the most comprehensive survey of the contribution of structural variants to the genetic risk of Parkinsons disease to date, this study highlights the need for large-scale long-read datasets to fully elucidate the role of structural variants in Parkinsons disease.

genetics↗

bioRxiv2022

A genome-wide mutational constraint map quantified from variation in 76,156 human genomes

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, but attempts to assess constraint for non-protein-coding regions have proven more difficult. Here we aggregate, process, and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD), the largest public open-access human genome reference dataset, and use this dataset to build a mutational constraint map for the whole genome. We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation across the genome. As expected, proteincoding sequences overall are under stronger constraint than non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association, and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, while non-coding constraint captures additional functional information underrecognized by gene constraint metrics. We demonstrate that this genome-wide constraint map provides an effective approach for characterizing the non-coding genome and improving the identification and interpretation of functional human genetic variation.

genetics↗

bioRxiv2019

Functional annotation of rare structural variation in the human brain

Structural variants (SVs) contribute substantially to risk of many brain related disorders including autism and schizophrenia. However, annotating the potential contribution of SVs to disease remains a major challenge. Here, we integrated high resolution SV calling from genome-sequencing in 755 human post-mortem brains with dorsal lateral prefrontal cortex RNA-sequencing from a subset of 629 samples to quantify the dosage and regulatory effects of SVs. We show that genic (p = 5.44x10-9) and regulatory SVs (enhancer p = 3.22x10-23, CTCF p = 3.86x10-18) are present at significantly lower frequencies than intergenic SVs after correcting for SV length. Copy number variants (CNVs)--deletions and duplications--exhibit a significant quantitative and directional relationship between the proportion of genic and regulatory content altered and gene expression, and the size of the effect is inversely correlated with the loss-of-function intolerance of the gene. We trained a joint linear model that leverages genic and regulatory annotations to predict expression effects of rare CNVs in independent samples (R2 = 0.21-0.41). We further developed a regulatory disruption score for each CNV that aggregates the predicted expression across all affected genes weighted by the genes intolerance score and applied it to an independent set of SVs from 14,891 genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders by ClinGen had significantly more extreme regulatory disruption scores than the rest of the SVs. Rank ordering based on the most extreme regulatory disruption scores prioritized pathogenic deletions that would not have been prioritized by frequency or length alone. This work points to the deleteriousness of regulatory SVs, particularly those altering CTCF sites. We further provide a simple approach for functionally annotating the regulatory effects of SVs in the human brain that has potential to be useful in larger SV studies and should improve as more regulatory annotation data is generated.

genomics↗

bioRxiv2019

Prioritization of genes driving congenital phenotypes of patients with de novo genomic structural variants

BackgroundGenomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients with multiple congenital abnormalities and/or intellectual disability carrying de novo SVs are frequently unknown.\n\nResultsWe applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with de novo SVs and an inconclusive diagnosis after regular genetic testing. In seven of these cases (18%) whole genome sequencing analysis detected disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict effects on genes directly affected by SVs and on genes indirectly affected due to changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In eight cases evidence was found for involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to a collection of 382 patients with previously detected and classified de novo SVs and identified candidate driver genes in 210 cases (54%), including 32 cases whose SVs were previously not classified as pathogenic. Pathogenic positional effects were predicted in 25% of the cases with balanced SVs and in 8% of the cases with copy number variants.\n\nConclusionsThese results show that driver gene prioritization based on integrative analysis of WGS data with phenotype association and chromatin organization datasets can improve the molecular diagnosis of patients with de novo SVs.

genomics↗

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

Genome-encoded Cytoplasmic Double-Stranded RNAs, Found in C9ORF72 ALS-FTD Brain, Provoke Propagated Neuronal Death

Innate immune signaling activation and DNA damage are pathological hallmarks of aging that may herald multiple adult-onset neurodegenerative diseases. Here, we report that both cell autonomous and non-autonomous neuronal death are triggered by the production of cytoplasmic double-stranded RNA (cdsRNA) from a regulated, disarticulated transgene in the setting of type I interferon (IFN-I) signaling. CdsRNA is a pathogen associated molecular pattern that induces IFN-I in many cell types. Transfection of a dsRNA mimetic into cultured human neurons also induces IFN-I signaling and cell death in a dose-dependent manner. Direct relevance to human disease is found in neurons of ALS-FTD patients carrying C9ORF72 intronic hexanucleotide expansions; cdsRNA isolated from these tissues is comprised of repeat sequences. Together, these findings implicate cdsRNA generated from genomic sequences in neurons as a trigger for sterile, viral-mimetic IFN-I induction and propagated neuronal death within in a neural circuit in the aging nervous system.

neuroscience↗