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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.

Background 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. Results 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. Conclusions 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 .

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.

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.

Atherosclerotic lesions in mice and humans switch on a ‘don’t eat me’ signal—expression of CD47—that prevents effective removal of diseased tissue; anti-CD47 antibody therapy can normalize this defective efferocytosis, with beneficial results in several mouse models of atherosclerosis. Pathogenesis mechanisms in atherosclerosis Nicholas Leeper and colleagues demonstrate that atherosclerotic lesions in mice and humans switch on a 'don't eat me' signal — in the form of expression of the anti-phagocytic transmembrane protein CD47 — that prevents effective removal of diseased tissue. They show that administration of anti-CD47 antibodies can normalize this defective phagocytic clearance (or ‘efferocytosis’) with beneficial results in several mouse models of atherosclerosis. These results suggest that targeted pro-efferocytic therapies could have potential in cardiovascular diseases. Atherosclerosis is the disease process that underlies heart attack and stroke 1 . Advanced lesions at risk of rupture are characterized by the pathological accumulation of diseased vascular cells and apoptotic cellular debris 2 . Why these cells are not cleared remains unknown 3 . 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’ 4 , 5 , 6 , 7 . 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 5 , impaired efferocytosis appears to play a pathogenic role in cardiovascular disease, but is not a fixed defect and may represent a novel therapeutic target.

Cellular genetic heterogeneity is common in many biological conditions including cancer, microbiome, and co-infection of multiple pathogens. Detecting and phasing minor variants play an instrumental role in deciphering cellular genetic heterogeneity, but they are still difficult tasks because of technological limitations. Recently, long-read sequencing technologies, including those by Pacific Biosciences and Oxford Nanopore, provide an opportunity to tackle these challenges. However, high error rates make it difficult to take full advantage of these technologies. To fill this gap, we introduce iGDA, an open-source tool that can accurately detect and phase minor single-nucleotide variants (SNVs), whose frequencies are as low as 0.2%, from raw long-read sequencing data. We also demonstrate that iGDA can accurately reconstruct haplotypes in closely related strains of the same species (divergence ≥0.011%) from long-read metagenomic data. Cellular genetic heterogeneity is common across biological conditions, yet application of long-read sequencing to this subject is limited by error rates. Here, the authors present iGDA, a tool for detection and phasing of minor variants from long-read sequencing data, allowing accurate reconstruction of haplotypes.

Key Points Biological research is becoming ever more information-driven, with individual laboratories now capable of generating terabyte scales of data in days. Supercomputing resources will be increasingly needed to get the most from the big data sets that researchers generate or analyse. The big data revolution in biology is matched by a revolution in high-performance computing that is making supercomputing resources available to anyone with an internet connection. A number of challenges are posed by large-scale data analysis, including data transfer (bringing the data and computational resources together), controlling access to the data, managing the data, standardizing data formats and integrating data of multiple different types to accurately model biological systems. New computational solutions that are readily available to all can aid in addressing these challenges. These solutions include cloud-based computing and high-speed, low-cost heterogeneous computational environments. Taking advantage of these resources requires a thorough understanding of the data and the computational problem. Knowing the parallelization of the analysis algorithms enables a more efficient solution to a computational problem by distributing tasks over many computer processors. The types of parallelism can be classified into two broad categories: loosely coupled (or coarse-grained) parallelism and tightly coupled (or fine-grained) parallelism, each benefiting from different types of computational platforms, depending on the problem of interest. Clusters of computers can be optimized for many different classes of computationally intense applications, such as sequence alignment, genome-wide association tests and reconstruction of Bayesian networks. Cloud computing makes cluster-based computing more accessible and affordable for all. The distributed computing paradigm MapReduce has been designed for cloud-based computing to solve problems such as mapping raw DNA sequence reads to a reference genome (that is, problems that have loosely coupled parallelism). Cloud computing provides a highly flexible, low-cost computational environment. However, the costs of cloud computing include sacrificing control of the underlying hardware and requiring that big data sets be transferred into the cloud for processing. Heterogeneous multi-core computational systems, such as graphics processing units (GPUs), are complementary to cloud-based computing and operate as low-cost, specialized accelerators that can increase peak arithmetic throughput by 10-fold to 100-fold. These systems are specifically tuned to efficiently solve problems involving massive tightly coupled parallelism. Heterogeneous computing provides a low-cost, flexible computational environment that improves performance and efficiency by exposing architectural features to programmers. However, programming applications to run in these environments requires significant informatics expertise. Cloud providers such as Microsoft make advanced cloud computing resources freely available to individual researchers through a competitive, peer-reviewed granting process. Others providers, such as Amazon, provide advanced cloud storage and computational resources via an intuitive and simple web interface. Users of Amazon Web Services can today not only upload big data sets and analysis tools to Amazon S3 but also solve problems using MapReduce via a point-and-click interface. This Review describes the different types of computational environments — such as cloud and heterogeneous computing — that are increasingly being used by life scientists to manage and analyse large multidimensional data sets. Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist — such as cloud and heterogeneous computing — to successfully tackle our big data problems.

Chronic Obstructive Pulmonary Disease (COPD) is a complex disease. Genetic, epigenetic, and environmental factors are known to contribute to COPD risk and disease progression. Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified EPAS1 as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity. EPAS1 is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes. Genes predicted to be regulated by EPAS1 were enriched for biological processes including signaling, cell communications, and system development. We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that Epas1 gene expression is reduced in mice chronically exposed to cigarette smoke. As EPAS1 downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells, we tested EPAS1 function in human endothelial cells. EPAS1 knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with EPAS1 downstream genes in lung tissue including hypoxia responsive genes, and genes associated with emphysema severity. Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a 'causal' role in the molecular pathophysiology of COPD, but it can be leveraged to directly identify novel key mediators of this pathophysiology.

While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells. Typical single-cell RNAseq pipelines will subcluster homogeneous cells. Here, authors present a computational algorithm for accurately identifying cell-type marker genes in single-cell data analysis with a low false discovery rate.