Institute for Quantitative Biomedical science

Dartmouth, Lebanon

Institute for Quantitative Biomedical science

Dartmouth, Lebanon
SEARCH FILTERS
Time filter
Source Type

Frost H.R.,Institute for Quantitative Biomedical science
Computational and Mathematical Methods in Medicine | Year: 2015

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P<0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data. © 2015 M. Tanveer Talukdar et al.


PubMed | Institute for Quantitative Biomedical science
Type: | Journal: Computational and mathematical methods in medicine | Year: 2015

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P < 0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data.


Felder R.A.,University of Virginia | White M.J.,Vanderbilt University | White M.J.,Institute for Quantitative Biomedical science | Williams S.M.,Vanderbilt University | And 3 more authors.
Current Opinion in Nephrology and Hypertension | Year: 2013

Purpose of Review: One-third of the world's population has hypertension and it is responsible for almost 50% of deaths from stroke or coronary heart disease. These statistics do not distinguish salt-sensitive from salt-resistant hypertension or include normotensives who are salt-sensitive even though salt sensitivity, independent of blood pressure, is a risk factor for cardiovascular and other diseases, including cancer. This review describes new personalized diagnostic tools for salt sensitivity. Recent Findings: The relationship between salt intake and cardiovascular risk is not linear, but rather fits a J-shaped curve relationship. Thus, a low-salt diet may not be beneficial to everyone and may paradoxically increase blood pressure in some individuals. Current surrogate markers of salt sensitivity are not adequately sensitive or specific. Tests in the urine that could be surrogate markers of salt sensitivity with a quick turn-around time include renal proximal tubule cells, exosomes, and microRNA shed in the urine. Summary: Accurate testing of salt sensitivity is not only laborious but also expensive, and with low patient compliance. Patients who have normal blood pressure but are salt-sensitive cannot be diagnosed in an office setting and there are no laboratory tests for salt sensitivity. Urinary surrogate markers for salt sensitivity are being developed. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.


Cowper-Sallari R.,Norris Cotton Cancer Center | Cowper-Sallari R.,Institute for Quantitative Biomedical science | Zhang X.,Norris Cotton Cancer Center | Zhang X.,Institute for Quantitative Biomedical science | And 10 more authors.
Nature Genetics | Year: 2012

Genome-wide association studies (GWAS) have identified thousands of SNPs that are associated with human traits and diseases. But, because the vast majority of these SNPs are located in non-coding regions of the genome, the mechanisms by which they promote disease risk have remained elusive. Employing a new methodology that combines cistromics, epigenomics and genotype imputation, we annotate the non-coding regions of the genome in breast cancer cells and systematically identify the functional nature of SNPs associated with breast cancer risk. Our results show that breast cancer risk-associated SNPs are enriched in the cistromes of FOXA1 and ESR1 and the epigenome of histone H3 lysine 4 monomethylation (H3K4me1) in a cancer-and cell type-specific manner. Furthermore, the majority of the risk-associated SNPs modulate the affinity of chromatin for FOXA1 at distal regulatory elements, thereby resulting in allele-specific gene expression, which is exemplified by the effect of the rs4784227 SNP on the TOX3 gene within the 16q12.1 risk locus. © 2012 Nature America, Inc. 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.


Moore J.H.,Quantitative Medicine | Lari R.C.S.,Institute for Quantitative Biomedical science | Hill D.,Quantitative Medicine | Hibberd P.L.,Massachusetts General Hospital | Madan J.C.,Dartmouth Hitchcock Medical Center
Pacific Symposium on Biocomputing 2011, PSB 2011 | Year: 2011

High-throughput sequencing technology has opened the door to the study of the human microbiome and its relationship with health and disease. This is both an opportunity and a significant biocomputing challenge. We present here a 3D visualization methodology and freely-available software package for facilitating the exploration and analysis of high-dimensional human microbiome data. Our visualization approach harnesses the power of commercial video game development engines to provide an interactive medium in the form of a 3D heat map for exploration of microbial species and their relative abundance in different patients. The advantage of this approach is that the third dimension provides additional layers of information that cannot be visualized using a traditional 2D heat map. We demonstrate the usefulness of this visualization approach using microbiome data collected from a sample of premature babies with and without sepsis. © 2011 World Scientific Publishing Co. Pte. Ltd.


Magnani L.,Institute for Quantitative Biomedical science | Eeckhoute J.,University of Lille Nord de France | Eeckhoute J.,Institute Pasteur Of Lille | Lupien M.,Institute for Quantitative Biomedical science
Trends in Genetics | Year: 2011

Chromatin is a well-known obstacle to transcription as it controls DNA accessibility, which directly impacts the recruitment of the transcriptional machinery. The recent burst of functional genomic studies provides new clues as to how transcriptional competency is regulated in this context. In this review, we discuss how these studies have shed light on a specialized subset of transcription factors, defined as pioneer factors, which direct recruitment of downstream transcription factors to establish lineage-specific transcriptional programs. In particular, we present evidence of an interplay between pioneer factors and the epigenome that could be central to this process. Finally, we discuss how pioneer factors, whose expression and function are altered in tumors, are also being considered for their prognostic value and should therefore be regarded as potential therapeutic targets. Thus, pioneer factors emerge as key players that connect the epigenome and transcription in health and disease. © 2011 Elsevier Ltd.


Tragante V.,University Utrecht | Moore J.H.,Norris Cotton Cancer Center | Moore J.H.,Institute for Quantitative Biomedical science | Asselbergs F.W.,University Utrecht | And 2 more authors.
Genetic Epidemiology | Year: 2014

The recently completed ENCODE project is a new source of information on metabolic activity, unveiling knowledge about evolution and similarities among species, refuting the myth that most DNA is "junk" and has no actual function. With this expansive resource comes a challenge: integrating these new layers of information into our current knowledge of single-nucleotide polymorphisms and previously described metabolic pathways with the aim of discovering new genes and pathways related to human diseases and traits. Further, we must determine which computational methods will be most useful in this pursuit. In this paper, we speculate over the possible methods that will emerge in this new, challenging field. © 2014 WILEY PERIODICALS, INC.


Urbanowicz R.J.,Institute for Quantitative Biomedical science | Kiralis J.,Institute for Quantitative Biomedical science | Fisher J.M.,Institute for Quantitative Biomedical science | Moore J.H.,Institute for Quantitative Biomedical science
BioData Mining | Year: 2012

Background: Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. Results: We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model's EDM and COR are each stronger predictors of model detection success than heritability. Conclusions: This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models. © 2012 Urbanowicz et al.; licensee BioMed Central Ltd.


Urbanowicz R.J.,Institute for Quantitative Biomedical science | Kiralis J.,Institute for Quantitative Biomedical science | Sinnott-Armstrong N.A.,Institute for Quantitative Biomedical science | Heberling T.,Institute for Quantitative Biomedical science | And 2 more authors.
BioData Mining | Year: 2012

Background: Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. Results: We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. Conclusions: GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis. © 2012 Urbanowicz et al.; licensee BioMed Central Ltd.

Loading Institute for Quantitative Biomedical science collaborators
Loading Institute for Quantitative Biomedical science collaborators