Biology subjects

Silverman, E. K.

Publications and source records attributed to Silverman, E. K..

28 records · Page 2Linked to original sources

bioRxiv2020

Unsupervised cluster analysis of SARS-CoV-2 genomes indicates that recent (June 2020) cases in Beijing are from a genetic subgroup that consists of mostly European and South(east) Asian samples, of which the latter are the most recent

Research efforts of the ongoing SARS-CoV-2 pandemic have focused on viral genome sequence analysis to understand how the virus spread across the globe. Here, we assess three recently identified SARS-CoV-2 genomes in Beijing from June 2020 and attempt to determine the origin of these genomes, made available in the GISAID database. The database contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Including the three new samples and excluding samples with missing annotations, we analyzed 7, 643 SARS-CoV-2 genomes. Using principal component analysis computed on a similarity matrix that compares all pairs of the SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index, we find that the newly discovered virus genomes from Beijing are in a genetic cluster that consists mostly of cases from Europe and South(east) Asia. The sequences of the new cases are most related to virus genomes from a small number of cases from China (March 2020), cases from Europe (February to early May 2020), and cases from South(east) Asia (May to June 2020). These findings could suggest that the original cases of this genetic cluster originated from China in March 2020 and were re-introduced to China by transmissions from samples from South(east) Asia between April and June 2020.

bioinformatics↗

bioRxiv2020

locStra: Fast analysis of regional/global stratification in whole genome sequencing (WGS) studies

locStra is an R-package for the analysis of regional and global population stratification in whole genome sequencing studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared to the global ones, based on user-defined window sizes and metrics, e.g. the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million RVs per subject), the runtime for the genome-wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.

genetics↗

bioRxiv2019

Integrated transcriptomic correlation network analysis identifies COPD molecular determinants

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called \"switch genes\" - for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

bioinformatics↗

bioRxiv2019

Analysis of genetically driven alternative splicing identifies FBXO38 as a novel COPD susceptibility gene

While many disease-associated single nucleotide polymorphisms (SNPs) are associated with gene expression (expression quantitative trait loci, eQTLs), a large proportion of complex disease genome-wide association study (GWAS) variants are of unknown function. Some of these SNPs may contribute to disease by regulating gene splicing. Here, we investigate whether SNPs that are associated with alternative splicing (splice QTL or sQTL) can identify novel functions for existing GWAS variants or suggest new associated variants in chronic obstructive pulmonary disease (COPD). RNA sequencing was performed on whole blood from 376 subjects from the COPDGene Study. Using linear models, we identified 561,060 unique sQTL SNPs associated with 30,333 splice sites corresponding to 6,419 unique genes. Similarly, 708,928 unique eQTL SNPs involving 15,913 genes were detected at 10% FDR. While there is overlap between sQTLs and eQTLs, 60% of sQTLs are not eQTLs. Co-localization analysis revealed that 7 out of 21 loci associated with COPD (p<1x10-6) in a published GWAS have at least one shared causal variant between the GWAS and sQTL studies. Among the genes identified to have splice sites associated with top GWAS SNPs was FBXO38, in which a novel exon was discovered to be protective against COPD. Importantly, the sQTL in this locus was validated by qPCR in both blood and lung tissue, demonstrating that splice variants relevant to lung tissue can be identified in blood. Other identified genes included CDK11A and SULT1A2. Overall, these data indicate that analysis of alternative splicing can provide novel insights into disease mechanisms. In particular, we demonstrated that SNPs in a known COPD GWAS locus on chromosome 5q32 influence alternative splicing in the gene FBXO38. Author SummaryWhile it is known that chronic obstructive pulmonary disease (COPD) is caused in part by genetic factors, few studies have identified specific causative genes. Genetic variants that alter the expression levels of genes have explained part of the genetic component of COPD, however, there are additional genetic variants with unknown function. In some genes the protein coding sequence can be altered by a mechanism known as RNA splicing. We hypothesized that some genetic variants that are associated with risk of COPD contribute to the disease by altering RNA splicing. In this study, we identified genetic variants that are associated both with COPD risk and RNA splicing. In particular, we found that a COPD associated variant of previously unknown function may contribute to the inclusion of a new exon in the FBXO38 gene. These finding are significant because they indicate that analysis of RNA splicing can help identify genes that contribute to disease.

genetics↗

bioRxiv2018

Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module

The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with new experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.

systems biology↗

bioRxiv2018

Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies

With advances in Whole Genome Sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and Sequence Kernel Association Test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally-efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-Set Mixed Model Association Tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program. SMMAT tests share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be only fit once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMAT tests correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

genetics↗

bioRxiv2017

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways relationship to T2D.

systems biology↗

bioRxiv2017

Systemic Markers of Adaptive and Innate Immunity Are Associated with COPD Severity and Spirometric Disease Progression

The progression of chronic obstructive pulmonary disease (COPD) is associated with marked alterations in circulating immune cell populations, but no studies have characterized alterations in these cell types across the full spectrum of lung function impairment in current and former smokers. In 6,299 subjects from the COPDGene and ECLIPSE studies, we related Coulter blood counts and proportions to cross-sectional FEV1 adjusting for current smoking status. We also related cell count measures to three-year change in FEV1 in ECLIPSE subjects. In a subset of subjects with blood gene expression data, we used cell type deconvolution methods to infer the proportions of immune cell subpopulations, and we related these to COPD clinical status. We observed that FEV1 levels are positively correlated with lymphocytes and negatively correlated with myeloid populations such as neutrophils and monocytes. In multivariate models, absolute cell counts and proportions were associated with cross-sectional FEV1, and lymphocytes, monocytes, and eosinophil counts were predictive of three-year change in lung function. Using cell type deconvolution to study immune cell subpopulations, we observed that subjects with COPD had a lower proportion of CD4+ resting memory cells and naive B cells compared to non-COPD smokers. Alterations in circulating immune cells in COPD support a mixed pattern of lymphocyte suppression and an enhanced myeloid cell immune response. Cell counts and proportions contribute independent information to models predicting lung function suggesting a critical role for immune response in long-term COPD outcomes. Cell type deconvolution is a promising method for immunophenotyping in large cohorts.

genomics↗

bioRxiv2017

Integrative genomics analysis identifies ACVR1B as a candidate causal gene of emphysema distribution in non-alpha 1-antitrypsin deficient smokers

BackgroundSeveral genetic risk loci associated with emphysema apico-basal distribution (EABD) have been identified through genome-wide association studies (GWAS), but the biological functions of these variants are unknown. To characterize gene regulatory functions of EABD-associated variants, we integrated EABD GWAS results with 1) a multi-tissue panel of expression quantitative trait loci (eQTL) from subjects with COPD and the GTEx project and 2) epigenomic marks from 127 cell types in the Roadmap Epigenomics project. Functional validation was performed for a variant near ACVR1B.\n\nResultsSNPs from 168 loci with P-values<5x10-5 in the largest GWAS meta-analysis of EABD (Boueiz A. et al, AJRCCM 2017) were analyzed. 54 loci overlapped eQTL regions from our multi-tissue panel, and 7 of these loci showed a high probability of harboring a single, shared GWAS and eQTL causal variant (colocalization posterior probability[≥]0.9). 17 cell types exhibited greater than expected overlap between EABD loci and DNase-I hypersensitive peaks, DNaseI hotspots, enhancer marks, or digital DNaseI footprints (permutation P-value < 0.05), with the strongest enrichment observed in CD4+, CD8+, and regulatory T cells. A region near ACVR1B demonstrated significant colocalization with a lung eQTL and overlapped DNase-I hypersensitive regions in multiple cell types, and reporter assays in human bronchial epithelial cells confirmed allele-specific regulatory activity for the lead variant, rs7962469.\n\nConclusionsIntegrative analysis highlights candidate causal genes, regulatory variants, and cell types that may contribute to the pathogenesis of emphysema distribution. These findings will enable more accurate functional validation studies and better understanding of emphysema distribution biology.

genomics↗

bioRxiv2016

Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

systems biology↗