Indirect Association.

<p>Genotyped SNPs often lie in a region of high linkage disequilibrium with an influential allele. The genotyped SNP will be statistically associated with disease as a surrogate for the disease SNP through an indirect association.</p

Eleven quick tips for architecting biomedical informatics workflows with cloud computing

<div><p>Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based workflows offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for architecting biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world’s largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction.</p></div

Linkage and Linkage Disequilibrium.

<p>Within a family, linkage occurs when two genetic markers (points on a chromosome) remain linked on a chromosome rather than being broken apart by recombination events during meiosis, shown as red lines. In a population, contiguous stretches of founder chromosomes from the initial generation are sequentially reduced in size by recombination events. Over time, a pair of markers or points on a chromosome in the population move from linkage disequilibrium to linkage equilibrium, as recombination events eventually occur between every possible point on the chromosome.</p

Chapter 11: Genome-Wide Association Studies

<div><p>Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.</p> </div

Spectrum of Disease Allele Effects.

<p>Disease associations are often conceptualized in two dimensions: allele frequency and effect size. Highly penetrant alleles for Mendelian disorders are extremely rare with large effect sizes (upper left), while most GWAS findings are associations of common SNPs with small effect sizes (lower right). The bulk of discovered genetic associations lie on the diagonal denoted by the dashed lines.</p

Phenotypic Robustness and the Assortativity Signature of Human Transcription Factor Networks

<div><p>Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs — such as their degree distribution — with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.</p></div

TFN models incorporating 81 different assortativity signatures highlight out-out assortativity as driving the robustness of dense TFNs.

<p>Each assortativity signature contains a different combination of the four types of assortativity where (). We built 1000 TFN models for each signature, and measured their robustness. Signatures in each column are sorted top-to-bottom in decreasing order by the average robustness of the 1000 TFN models. Faded signatures are not significantly different from the average robustness of random TFN models (paired <i>t</i>-test; significant Bonferroni-corrected ). Yellow highlights signatures where and blue highlights signatures where . The orange lines correspond to the signature that is most similar to the average human signature (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003780#pcbi-1003780-g003" target="_blank">Fig. 3</a>).</p

Of the four components of the assortativity signature, out-out assortativity is the strongest predictor of robustness in dense TFN models.

<p>Simple linear regression was used to explain the variation in the average robustness for the 81 test signatures (as shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003780#pcbi-1003780-g004" target="_blank">Fig. 4</a>). For each , the Z-score for each assortativity type was used as the lone explanatory variable, resulting in a total of 16 linear models. Black points represent positive slopes of best fit lines (<i>e.g.</i>, see inset), and red points represent negative slopes. Slopes are significant (asterisks) if (Bonferroni-corrected, ).</p

Dense TFN models that possess the human assortativity signature are more robust than random models.

<p>Z-scores for the four types of assortativity are represented as signatures, as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003780#pcbi-1003780-g002" target="_blank">Fig. 2</a>. The average human assortativity signature was computed from the signatures of the 41 human TFNs, and is represented as a blue line. For each average out-degree , 1000 TFN models () were generated to approximate the human signature, and the resulting signatures are shown as orange lines. For each TFN model, we constructed 1000 randomly-rewired null models for computing Z-scores. Box-and-whisker plots show the robustness for the 1000 TFN models that approximate the human signature (orange) compared to 1000 random models (grey). For , , and for all other , (paired <i>t</i>-test).</p

Constructing human transcription factor networks (TFNs) from genome-wide DNase I hypersensitivity profiles and motif analysis.

<p>(A) The <i>cis</i>-regulatory regions of DNA directly upstream of the genes encoding hypothetical TFs (TF-<i>A</i>, TF-<i>B</i>, and TF-<i>C</i>) contain DNase I hypersensitive sites that are accessible to protein binding. The evidence for binding events are the DNase I resistant footprints within the hypersensitive sites. Although the identity of the protein that leaves a footprint is not directly observed, the recognition of a TF-specific DNA binding motif enables the inference of which TF is bound at that footprint. In this hypothetical example, binding sites for both TF-<i>B</i> and TF-<i>C</i> are found within footprints in DNase I hypersensitive sites upstream of the gene encoding TF-<i>A</i>. Therefore, TF-<i>B</i> and TF-<i>C</i> are inferred to be bound upstream of the gene for TF-<i>A</i>. Likewise, TF-<i>B</i> and TF-<i>C</i> are bound upstream of each other's genes. (B) These inferred binding events are represented as directed edges in the TFN, <i>i.e.</i>, , , , and . The dynamics of this TFN can be modeled using a Boolean framework, as follows. The state of each TF is considered either off or on at any given time, and regulatory rules (shown here as truth tables) dictate the future states of TFs based on their current states. (C) The regulatory rules for the entire TFN model is its genotype. (D) The states of all the TFs in the TFN model at a particular time is referred to as its configuration at that time. Given an initial configuration, the configuration at each subsequent time point is updated according to the genotype. The TFN model has a finite number of possible configurations, and the genotype synchronously and deterministically updates one to the next. Therefore, the TFN model inevitably encounters an indefinitely repeating cycle of configurations, which represents the model's phenotype.</p