Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, ...in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell. One ...consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.
As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to ...understanding disease biology and regulatory genetics.
We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets.
Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition .
Prokaryotic DNA contains three types of methylation: N6-methyladenine, N4-methylcytosine and 5-methylcytosine. The lack of tools to analyse the frequency and distribution of methylated residues in ...bacterial genomes has prevented a full understanding of their functions. Now, advances in DNA sequencing technology, including single-molecule, real-time sequencing and nanopore-based sequencing, have provided new opportunities for systematic detection of all three forms of methylated DNA at a genome-wide scale and offer unprecedented opportunities for achieving a more complete understanding of bacterial epigenomes. Indeed, as the number of mapped bacterial methylomes approaches 2,000, increasing evidence supports roles for methylation in regulation of gene expression, virulence and pathogen-host interactions.
The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. ...To characterize molecular systems associated with late-onset Alzheimer’s disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.
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•Systems approach to LOAD based on large-scale human brain-tissue sampling•Molecular networks show strong remodeling effect in LOAD brains•Integrative network-based analysis implicates the immune/microglia network in LOAD•TYROBP implicated as key causal regulator within the immune/microglia module
An integrated systems approach leverages transcriptome data from postmortem brains of late-onset Alzheimer’s disease patients to identify key nodes that drive dysregulated or rewired networks in the disease state.
Investigators have long suspected that pathogenic microbes might contribute to the onset and progression of Alzheimer’s disease (AD) although definitive evidence has not been presented. Whether such ...findings represent a causal contribution, or reflect opportunistic passengers of neurodegeneration, is also difficult to resolve. We constructed multiscale networks of the late-onset AD-associated virome, integrating genomic, transcriptomic, proteomic, and histopathological data across four brain regions from human post-mortem tissue. We observed increased human herpesvirus 6A (HHV-6A) and human herpesvirus 7 (HHV-7) from subjects with AD compared with controls. These results were replicated in two additional, independent and geographically dispersed cohorts. We observed regulatory relationships linking viral abundance and modulators of APP metabolism, including induction of APBB2, APPBP2, BIN1, BACE1, CLU, PICALM, and PSEN1 by HHV-6A. This study elucidates networks linking molecular, clinical, and neuropathological features with viral activity and is consistent with viral activity constituting a general feature of AD.
•Common viral species frequently detected in normal, aging brain•Increased HHV-6A and HHV-7 in brains of subjects with Alzheimer’s disease (AD)•Findings were replicated in two additional, independent cohorts•Multiscale networks reveal viral regulation of AD risk, and APP processing genes
Readhead et al. construct multiscale networks of the late-onset Alzheimer’s disease (AD)-associated virome and observe pathogenic regulation of molecular, clinical, and neuropathological networks by several common viruses, particularly human herpesvirus 6A and human herpesvirus 7.
A window into third-generation sequencing Schadt, Eric E.; Turner, Steve; Kasarskis, Andrew
Human molecular genetics,
10/2010, Volume:
19, Issue:
R2
Journal Article
Peer reviewed
Open access
First- and second-generation sequencing technologies have led the way in revolutionizing the field of genomics and beyond, motivating an astonishing number of scientific advances, including enabling ...a more complete understanding of whole genome sequences and the information encoded therein, a more complete characterization of the methylome and transcriptome and a better understanding of interactions between proteins and DNA. Nevertheless, there are sequencing applications and aspects of genome biology that are presently beyond the reach of current sequencing technologies, leaving fertile ground for additional innovation in this space. In this review, we describe a new generation of single-molecule sequencing technologies (third-generation sequencing) that is emerging to fill this space, with the potential for dramatically longer read lengths, shorter time to result and lower overall cost.
Inborn errors of metabolism (IEM) are not unlike common diseases. They often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. ...This greatly impacts patient care and reveals fundamental gaps in our knowledge of disease modifying biology. Systems biology approaches that integrate multi-omics data into molecular networks have significantly improved our understanding of complex diseases. Similar approaches to study IEM are rare despite their complex nature. We highlight that existing common disease-derived datasets and networks can be repurposed to generate novel mechanistic insight in IEM and potentially identify candidate modifiers. While understanding disease pathophysiology will advance the IEM field, the ultimate goal should be to understand per individual how their phenotype emerges given their primary mutation on the background of their whole genome, not unlike personalized medicine. We foresee that panomics and network strategies combined with recent experimental innovations will facilitate this.
Inborn errors of metabolism (IEM) often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. Argmann et al. highlight how approaches that integrate multi-omics data into molecular networks may advance the IEM field by revealing novel mechanistic insights and candidate modifier genes.
Atherosclerosis is the disease process that underlies heart attack and stroke. Advanced lesions at risk of rupture are characterized by the pathological accumulation of diseased vascular cells and ...apoptotic cellular debris. Why these cells are not cleared remains unknown. Here we show that atherogenesis is associated with upregulation of CD47, a key anti-phagocytic molecule that is known to render malignant cells resistant to programmed cell removal, or 'efferocytosis'. We find that administration of CD47-blocking antibodies reverses this defect in efferocytosis, normalizes the clearance of diseased vascular tissue, and ameliorates atherosclerosis in multiple mouse models. Mechanistic studies implicate the pro-atherosclerotic factor TNF-α as a fundamental driver of impaired programmed cell removal, explaining why this process is compromised in vascular disease. Similar to recent observations in cancer, impaired efferocytosis appears to play a pathogenic role in cardiovascular disease, but is not a fixed defect and may represent a novel therapeutic target.
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