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Darabos C.,Computational Genetics Laboratory | Darabos C.,University of Southern New Hampshire | Di Cunto F.,University of Turin | Tomassini M.,University of Lausanne | And 4 more authors.
PLoS ONE | Year: 2011

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate. © 2011 Darabos et al.


Gui J.,Norris Cotton Cancer Center | Moore J.H.,Norris Cotton Cancer Center | Moore J.H.,Computational Genetics Laboratory | Moore J.H.,University of New Hampshire | And 7 more authors.
Human Genetics | Year: 2011

The widespread use of high-throughput methods of single nucleotide polymorphism (SNP) genotyping has created a number of computational and statistical challenges. The problem of identifying SNP-SNP interactions in case-control studies has been studied extensively, and a number of new techniques have been developed. Little progress has been made, however, in the analysis of SNP-SNP interactions in relation to time-to-event data, such as patient survival time or time to cancer relapse. We present an extension of the two class multifactor dimensionality reduction (MDR) algorithm that enables detection and characterization of epistatic SNP-SNP interactions in the context of survival analysis. The proposed Survival MDR (Surv-MDR) method handles survival data by modifying MDR's constructive induction algorithm to use the log-rank test. Surv-MDR replaces balanced accuracy with log-rank test statistics as the score to determine the best models. We simulated datasets with a survival outcome related to two loci in the absence of any marginal effects. We compared Surv-MDR with Cox-regression for their ability to identify the true predictive loci in these simulated data. We also used this simulation to construct the empirical distribution of Surv-MDR's testing score. We then applied Surv-MDR to genetic data from a population-based epidemiologic study to find prognostic markers of survival time following a bladder cancer diagnosis. We identified several two-loci SNP combinations that have strong associations with patients' survival outcome. Surv-MDR is capable of detecting interaction models with weak main effects. These epistatic models tend to be dropped by traditional Cox regression approaches to evaluating interactions. With improved efficiency to handle genome wide datasets, Surv-MDR will play an important role in a research strategy that embraces the complexity of the genotype-phenotype mapping relationship since epistatic interactions are an important component of the genetic basis of disease. © 2010 Springer-Verlag.


Gilbert-Diamond D.,Computational Genetics Laboratory | Moore J.H.,Computational Genetics Laboratory | Moore J.H.,University of New Hampshire | Moore J.H.,University of Vermont | Moore J.H.,Dartmouth College
Current Protocols in Human Genetics | Year: 2011

The goal of this unit is to introduce gene-gene interactions (epistasis) as a significant complicating factor in the search for disease susceptibility genes. This unit begins with an overview of genegene interactions and why they are likely to be common. Then, it reviews several statistical and computational methods for detecting and characterizing genes with effects that are dependent on other genes. The focus of this unit is genetic association studies of discrete and quantitative traits because most of the methods for detecting gene-gene interactions have been developed specifically for these study designs. © 2011 by John Wiley & Sons, Inc.


McAllister T.W.,Section of Neuropsychiatry | Tyler A.L.,Computational Genetics Laboratory | Flashman L.A.,Section of Neuropsychiatry | Rhodes C.H.,Section of Molecular Pathology | And 7 more authors.
Journal of Neurotrauma | Year: 2012

Brain-derived neurotrophic factor (BDNF) plays a role in cognition, as well as neural survival and plasticity. There are several common polymorphisms in the BDNF gene, one of which (rs6265) is an extensively studied non-synonymous coding polymorphism (Val66Met) which has been linked to cognitive performance in healthy controls and some clinical populations. We hypothesized that the Met allele of rs6265 would be associated with poorer cognitive performance in individuals with mild-to-moderate traumatic brain injury, and that other polymorphisms in the BDNF gene would also affect cognition. Genotype at 9 single-nucleotide polymorphisms (SNPs) in the BDNF gene, and measures of speed of information processing, learning, and memory were assessed in 75 patients with mTBI and 38 healthy subjects. Consistent with previous reports, the Met allele of rs6265 was associated with cognition (slower processing speed) in the entire group. Two other SNPs were associated with processing speed in the mTBI group, but both are in linkage disequilibrium with rs6265, and neither remained significant after adjustment for rs6265 status. Within the mTBI group, but not the controls, 4 SNPs, but not rs6265, were associated with memory measures. These associations were not affected by adjustment for rs6265 status. Polymorphisms in BDNF influence cognitive performance shortly after mTBI. The results raise the possibility that a functional polymorphism other than rs6265 may contribute to memory function after mTBI. © Mary Ann Liebert, Inc.


Hu T.,Computational Genetics Laboratory | Payne J.L.,Computational Genetics Laboratory | Banzhaf W.,Memorial University of Newfoundland | Moore J.H.,Computational Genetics Laboratory
Genetic Programming and Evolvable Machines | Year: 2012

Redundancy is a ubiquitous feature of genetic programming (GP), with many-to-one mappings commonly observed between genotype and phenotype, and between phenotype and fitness. If a representation is redundant, then neutral mutations are possible. A mutation is phenotypically-neutral if its application to a genotype does not lead to a change in phenotype. A mutation is fitness-neutral if its application to a genotype does not lead to a change in fitness. Whether such neutrality has any benefit for GP remains a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, such as success rate or search efficiency, to investigate the utility of neutrality in GP. Here, we take a different tack and use a measure of robustness to quantify the neutrality associated with each genotype, phenotype, and fitness value. We argue that understanding the influence of neutrality on GP requires an understanding of the distributions of robustness at these three levels, and of the interplay between robustness, evolvability, and accessibility amongst genotypes, phenotypes, and fitness values. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration and allows for the full characterization of these quantities, which we then relate to the dynamical properties of simple mutation-based evolutionary processes. Our results demonstrate that it is not only the distribution of robustness amongst phenotypes that affects evolutionary search, but also (1) the distributions of robustness at the genotypic and fitness levels and (2) the mutational biases that exist amongst genotypes, phenotypes, and fitness values. Of crucial importance is the relationship between the robustness of a genotype and its mutational bias toward other phenotypes. © Springer Science+Business Media, LLC 2012.


Hu T.,Computational Genetics Laboratory | Payne J.L.,Computational Genetics Laboratory | Banzhaf W.,Memorial University of Newfoundland | Moore J.H.,Computational Genetics Laboratory
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Whether neutrality has positive or negative effects on evolutionary search is a contentious topic, with reported experimental results supporting both sides of the debate. Most existing studies use performance statistics, e.g., success rate or search efficiency, to investigate if neutrality, either embedded or artificially added, can benefit an evolutionary algorithm. Here, we argue that understanding the influence of neutrality on evolutionary optimization requires an understanding of the interplay between robustness and evolvability at the genotypic and phenotypic scales. As a concrete example, we consider a simple linear genetic programming system that is amenable to exhaustive enumeration, and allows for the full characterization of these properties. We adopt statistical measurements from RNA systems to quantify robustness and evolvability at both genotypic and phenotypic levels. Using an ensemble of random walks, we demonstrate that the benefit of neutrality crucially depends upon its phenotypic distribution. © 2011 Springer-Verlag.


Payne J.L.,Computational Genetics Laboratory | Sinnott-Armstrong N.A.,Computational Genetics Laboratory | Moore J.H.,Computational Genetics Laboratory
Interdisciplinary Sciences: Computational Life Sciences | Year: 2010

Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the standard workhorses of scientific computing. With the recent release of generalpurpose GPUs and NVIDIA's GPU programming language, CUDA, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. The goal of this article is to concisely present an introduction to GPU hardware and programming, aimed at the computational biologist or bioinformaticist. To this end, we discuss the primary differences between GPU and CPU architecture, introduce the basics of the CUDA programming language, and discuss important CUDA programming practices, such as the proper use of coalesced reads, data types, and memory hierarchies. We highlight each of these topics in the context of computing the all-pairs distance between instances in a dataset, a common procedure in numerous disciplines of scientific computing. We conclude with a runtime analysis of the GPU and CPU implementations of the all-pairs distance calculation. We show our final GPU implementation to outperform the CPU implementation by a factor of 1700. © 2010 International Association of Scientists in the Interdisciplinary Areas and Springer-Verlag Berlin Heidelberg.


Thompson P.,Computational Genetics Laboratory | Thompson S.,Computational Genetics Laboratory
AAAI Fall Symposium - Technical Report | Year: 2012

In the context of two related NIH projects supporting scientific collaboration we seek to implement an environment for collaborative information retrieval and analysis based on utility theory. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.


Zhang X.,Institute for Quantitative Biomedical science | Cowper-Sal-lari R.,Institute for Quantitative Biomedical science | Cowper-Sal-lari R.,Computational Genetics Laboratory | Bailey S.D.,Ontario Cancer Institute | And 5 more authors.
Genome Research | Year: 2012

Genome-wide association studies (GWAS) are identifying genetic predisposition to various diseases. The 17q24.3 locus harbors the single nucleotide polymorphism (SNP) rs1859962 that is statistically associated with prostate cancer (PCa). It defines a 130-kb linkage disequilibrium (LD) block that lies in an ∼2-Mb gene desert area. The functional biology driving the risk associated with this LD block is unknown. Here, we integrate genome-wide chromatin landscape data sets, namely, epigenomes and chromatin openness from diverse cell types. This identifies a PCa-specific enhancer within the rs1859962 risk LD block that establishes a 1-Mb chromatin loop with the SOX9 gene. The rs8072254 and rs1859961 SNPs mapping to this enhancer impose allele-specific gene expression. The variant allele of rs8072254 facilitates androgen receptor (AR) binding driving increased enhancer activity. The variant allele of rs1859961 decreases FOXA1 binding while increasing AP-1 binding. The latter is key to imposing allele-specific gene expression. The rs8072254 variant in strong LD with the rs1859962 risk SNP can account for the risk associated with this locus, while rs1859961 is a rare variant less likely to contribute to the risk associated with this LD block. Together, our results demonstrate that multiple genetic variants mapping to a unique enhancer looping to the SOX9 oncogene can account for the risk associated with the PCa 17q24.3 locus. Allele-specific recruitment of the transcription factors androgen receptor (AR) and activating protein-1 (AP-1) account for the increased enhancer activity ascribed to this PCa-risk LD block. This further supports the notion that an integrative genomics approach can identify the functional biology disrupted by genetic risk variants.


Urbanowicz R.J.,Computational Genetics Laboratory | Granizo-Mackenzie D.,Computational Genetics Laboratory | Moore J.H.,Computational Genetics Laboratory
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Learning Classifier Systems (LCSs) are a unique brand of multifaceted evolutionary algorithms well suited to complex or heterogeneous problem domains. One such domain involves data mining within genetic association studies which investigate human disease. Previously we have demonstrated the ability of Michigan-style LCSs to detect genetic associations in the presence of two complicating phenomena: epistasis and genetic heterogeneity. However, LCSs are computationally demanding and problem scaling is a common concern. The goal of this paper was to apply and evaluate expert knowledge-guided covering and mutation operators within an LCS algorithm. Expert knowledge, in the form of Spatially Uniform ReliefF (SURF) scores, was incorporated to guide learning towards regions of the problem domain most likely to be of interest. This study demonstrates that expert knowledge can improve learning efficiency in the context of a Michigan-style LCS. © 2012 Springer-Verlag.

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