Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Phylogenomics illuminates the backbone of the Myriapoda Tree of Life and reconciles morphological and molecular phylogenies

© The Author(s) 2017 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The attached file is the published version of the article

Oral Rabies Vaccination in North America: Opportunities, Complexities, and Challenges

Steps to facilitate inter-jurisdictional collaboration nationally and continentally have been critical for implementing and conducting coordinated wildlife rabies management programs that rely heavily on oral rabies vaccination (ORV). Formation of a national rabies management team has been pivotal for coordinated ORV programs in the United States of America. The signing of the North American Rabies Management Plan extended a collaborative framework for coordination of surveillance, control, and research in border areas among Canada, Mexico, and the US. Advances in enhanced surveillance have facilitated sampling of greater scope and intensity near ORV zones for improved rabies management decision-making in real time. The value of enhanced surveillance as a complement to public health surveillance was best illustrated in Ohio during 2007, where 19 rabies cases were detected that were critical for the formulation of focused contingency actions for controlling rabies in this strategically key area. Diverse complexities and challenges are commonplace when applying ORV to control rabies in wild meso-carnivores. Nevertheless, intervention has resulted in notable successes, including the elimination of an arctic fox (Vulpes lagopus) rabies virus variant in most of southern Ontario, Canada, with ancillary benefits of elimination extending into Quebec and the northeastern US. Progress continues with ORV toward preventing the spread and working toward elimination of a unique variant of gray fox (Urocyon cinereoargenteus) rabies in west central Texas. Elimination of rabies in coyotes (Canis latrans) through ORV contributed to the US being declared free of canine rabies in 2007. Raccoon (Procyon lotor) rabies control continues to present the greatest challenges among meso-carnivore rabies reservoirs, yet to date intervention has prevented this variant from gaining a broad geographic foothold beyond ORV zones designed to prevent its spread from the eastern US. Progress continues toward the development and testing of new bait-vaccine combinations that increase the chance for improved delivery and performance in the diverse meso-carnivore rabies reservoir complex in the US

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

Expression QTLs Mapping and Analysis: A Bayesian Perspective.

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results

Natural infection by the protozoan Leptomonas wallacei impacts the morphology, physiology, reproduction, and lifespan of the insect Oncopeltus fasciatus

Trypanosomatids are protozoan parasites that infect thousands of globally dispersed hosts, potentially affecting their physiology. Several species of trypanosomatids are commonly found in phytophagous insects. Leptomonas wallacei is a gut-restricted insect trypanosomatid only retrieved from Oncopeltus fasciatus. The insects get infected by coprophagy and transovum transmission of L. wallacei cysts. The main goal of the present study was to investigate the effects of a natural infection by L. wallacei on the hemipteran insect O. fasciatus, by comparing infected and uninfected individuals in a controlled environment. The L. wallacei-infected individuals showed reduced lifespan and morphological alterations. Also, we demonstrated a higher infection burden in females than in males. The infection caused by L. wallacei reduced host reproductive fitness by negatively impacting egg load, oviposition, and eclosion, and promoting an increase in egg reabsorption. Moreover, we associated the egg reabsorption observed in infected females, with a decrease in the intersex gene expression. Finally, we suggest alterations in population dynamics induced by L. wallacei infection using a mathematical model. Collectively, our findings demonstrated that L. wallacei infection negatively affected the physiology of O. fasciatus, which suggests that L. wallacei potentially has a vast ecological impact on host population growth