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Jason H Moore

Publications and source records attributed to Jason H Moore.

At least 19 recordsLinked to original sources
PubMed2025

A decentralized future for the open-science databases.

The continuous and reliable open access to curated biological data repositories is indispensable for accelerating rigorous scientific inquiry and fostering reproducible research outcomes. However, the current paradigm, which relies heavily on centralized infrastructure for the storage and distribution of foundational biomedical datasets, inherently introduces significant vulnerabilities. This centralized model is susceptible to single points of failure, including cyberattacks, technical malfunctions, natural disasters, and even political or funding uncertainties. Such disruptions can lead to widespread data unavailability, data loss, integrity compromises, and substantial delays in critical research, ultimately impeding scientific progress. The downstream effect of such interruptions can be the widespread paralysis of diverse research activities, including computational, clinical, molecular, and climate studies. This scenario vividly illustrates the inherent dangers of consolidating essential scientific resources within a single geopolitical or institutional locus. As data generation is accelerating and the global landscape continues to fluctuate, the sustainability of centralized models must be critically re-evaluated. A shift toward federated and decentralized architectures may offer a robust and forward-looking approach to enhancing the resilience of scientific data infrastructures by reducing exposure to governance instability, infrastructural fragility, and funding volatility, while also promoting equity and global accessibility. Inspired by established models such as ELIXIR's federated infrastructure and the policy and funding frameworks developed by CODATA and the Global Biodata Coalition (GBC), emerging Decentralized Science (DeSci) initiatives can contribute to building more resilient, fair, and incentive-aligned data ecosystems. The future of open science depends on integrating these complementary approaches to establish a globally distributed, economically sustainable, and institutionally robust infrastructure that safeguards scientific data as a public good, further ensuring continued accessibility, interoperability, and preservation for generations to come. Here, we examine the structural limitations of centralized repositories, evaluate federated and decentralized models, and propose a hybrid framework for resilient, fair, and sustainable scientific data stewardship.

data accessibility↗
PubMed2006

Cytokine expression patterns associated with systemic adverse events following smallpox immunization.

Vaccinia virus is reactogenic in a significant number of vaccinees, with the most common adverse events being fever, lymphadenopathy, and rash. Although the inoculation is given in the skin, these adverse events suggest a robust systemic inflammatory response. To elucidate the cytokine response signature of systemic adverse events, we used a protein microarray technique to precisely quantitate 108 serum cytokines and chemokines in vaccine recipients before and 1 week after primary immunization with Aventis Pasteur smallpox vaccine. We studied 74 individuals after vaccination, of whom 22 experienced a systemic adverse event and 52 did not. The soluble factors most associated with adverse events were selected on the basis of voting among a committee of machine-learning methods and statistical procedures, and the selected cytokines were used to build a final decision-tree model. On the basis of changes in protein expression, we identified 6 cytokines that accurately discriminate between individuals on the basis of adverse event status: granulocyte colony-stimulating factor, stem cell factor, monokine induced by interferon-gamma (CXCL9), intercellular adhesion molecule-1, eotaxin, and tissue inhibitor of metalloproteinases-2. This cytokine signature is characteristic of particular inflammatory response pathways and suggests that the secretion of cytokines by fibroblasts plays a central role in systemic adverse events.

Adolescent↗
PubMed2006

A role for CETP TaqIB polymorphism in determining susceptibility to atrial fibrillation: a nested case control study.

BACKGROUND: Studies investigating the genetic and environmental characteristics of atrial fibrillation (AF) may provide new insights in the complex development of AF. We aimed to investigate the association between several environmental factors and loci of candidate genes, which might be related to the presence of AF. METHODS: A nested case-control study within the PREVEND cohort was conducted. Standard 12 lead electrocardiograms were recorded and AF was defined according to Minnesota codes. For every case, an age and gender matched control was selected from the same population (n = 194). In addition to logistic regression analyses, the multifactor-dimensionality reduction (MDR) method and interaction entropy graphs were used for the evaluation of gene-gene and gene-environment interactions. Polymorphisms in genes from the Renin-angiotensin, Bradykinin and CETP systems were included. RESULTS: Subjects with AF had a higher prevalence of electrocardiographic left ventricular hypertrophy, ischemic heart disease, hypertension, renal dysfunction, elevated levels of C-reactive protein (CRP) and increased urinary albumin excretion as compared to controls. The polymorphisms of the Renin-angiotensin system and Bradykinin gene did not show a significant association with AF (p > 0.05). The TaqIB polymorphism of the CETP gene was significantly associated with the presence of AF (p < 0.05). Using the MDR method, the best genotype-phenotype models included the combination of micro- or macroalbuminuria and CETP TaqIB polymorphism, CRP >3 mg/L and CETP TaqIB polymorphism, renal dysfunction and the CETP TaqIB polymorphism, and ischemic heart disease and CETP TaqIB polymorphism (1000 fold permutation testing, P < 0.05). Interaction entropy graph showed that the combination of albuminuria and CETP TaqIB polymorphism removed the most entropy. CONCLUSION: CETP TaqIB polymorphism is significantly associated with the presence of AF in the context of micro- or macroalbuminuria, elevated C-reactive protein, renal dysfunction, and ischemic heart disease.

Atrial Fibrillation↗
PubMed2006

Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data.

BACKGROUND: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex human disease. In the absence of detailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heterogeneity. The performance of three such methods--Bayesian Classification, Hypergraph-Based Clustering, and Fuzzy k-Modes Clustering--appropriate for categorical data were compared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heterogeneity and/or gene-gene interaction, which are two other complicating factors in discovering genetic models of complex human disease. To determine the efficacy of applying the Bayesian Classification method to real data, the reliability of its internal clustering metrics at finding good clusterings was evaluated using permutation testing. RESULTS: Bayesian Classification outperformed the other two methods, with the exception that the Fuzzy k-Modes Clustering performed best on the most complex genetic model. Bayesian Classification achieved excellent recovery for 75% of the datasets simulated under the simplest genetic model, while it achieved moderate recovery for 56% of datasets with a sample size of 500 or more (across all simulated models) and for 86% of datasets with 10 or fewer nonfunctional loci (across all simulated models). Neither Hypergraph Clustering nor Fuzzy k-Modes Clustering achieved good or excellent cluster recovery for a majority of datasets even under a restricted set of conditions. When using the average log of class strength as the internal clustering metric, the false positive rate was controlled very well, at three percent or less for all three significance levels (0.01, 0.05, 0.10), and the false negative rate was acceptably low (18 percent) for the least stringent significance level of 0.10. CONCLUSION: Bayesian Classification shows promise as an unsupervised computational method for dissecting trait heterogeneity in genotypic data. Its control of false positive and false negative rates lends confidence to the validity of its results. Further investigation of how different parameter settings may improve the performance of Bayesian Classification, especially under more complex genetic models, is ongoing.

Algorithms↗
PubMed2006

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.

Atrial Fibrillation↗
PubMed2006

Single-nucleotide polymorphisms for diagnosis of salt-sensitive hypertension.

BACKGROUND: Salt-sensitive (SS) hypertension affects >30 million Americans and is often associated with low plasma renin activity. We tested the diagnostic validity of several candidate genes for SS and low-renin hypertension. METHODS: In Japanese patients with newly diagnosed, untreated hypertension (n = 184), we studied polymorphisms in 10 genes, including G protein-coupled receptor kinase type 4 (GRK4), some variations of which are associated with hypertension and impair D1 receptor (D1R)-inhibited renal sodium transport. We used the multifactor dimensionality reduction method to determine the genotype associated with salt sensitivity (> or =10% increase in blood pressure with high sodium intake) or low renin. To determine whether the GRK4 genotype is associated with impaired D1R function, we tested the natriuretic effect of docarpamine, a dopamine prodrug, in normotensive individuals with or without GRK4 polymorphisms (n = 18). RESULTS: A genetic model based on GRK4 R65L, GRK4 A142V, and GRK4 A486V was 94.4% predictive of SS hypertension, whereas the single-locus model with only GRK4 A142V was 78.4% predictive, and a 2-locus model of GRK4 A142V and CYP11B2 C-344T was 77.8% predictive of low-renin hypertension. Sodium excretion was inversely related to the number of GRK4 variants in hypertensive persons, and the natriuretic response to dopaminergic stimulation was impaired in normotensive persons having > or =3 GRK4 gene variants. CONCLUSIONS: GRK4 gene variants are associated with SS and low-renin hypertension. However, the genetic model predicting SS hypertension is different from the model for low renin, suggesting genetic differences in these 2 phenotypes. Like low-renin testing, screening for GRK4 variants may be a useful diagnostic adjunct for detection of SS hypertension.

Asian People↗
PubMed2006

Machine learning for detecting gene-gene interactions: a review.

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

Algorithms↗
PubMed2006

Diabetic nephropathy is associated with gene expression levels of oxidative phosphorylation and related pathways.

The in vitro behavior of skin fibroblasts from patients with or without diabetic nephropathy is associated with diabetic nephropathy risk. Here we compared skin fibroblast gene expression profiles from two groups of type 1 diabetic patients: 20 with very fast ("fast-track") versus 20 with very slow ("slow-track") rates of development of diabetic nephropathy lesions. Gene expression profiles of skin fibroblasts grown in 25 mmol/l glucose for 36 h were assessed by Affymetrix HG-U133A GeneChips to determine the proportion of genes in a given biological pathway that were directionally consistent in their group differences. Five pathways reached statistical significance. All had significantly greater proportions of genes with higher expression levels in the fast-track group. These pathways, the first four of which are closely related and have overlapping genes, included oxidative phosphorylation (P<0.001), electron transport system complex III (P=0.017), citrate cycle (P=0.037), propanoate metabolism (P=0.044), and transcription factors (P=0.046). These results support the concept that oxidative phosphorylation and related upstream pathways may be important in the pathogenesis of diabetic nephropathy. Whether these findings reflect inherent genetic cellular characteristics, "cell memory," or both requires further study.

Adult↗
PubMed2006

Visual analysis of statistical results from microarray studies of human breast cancer.

Computational and statistical analysis of microarray data is a daunting challenge. Perhaps even more daunting is the biological interpretation of microarray data analysis results. We have previously developed the exploratory visual analysis (EVA) software and database for exploring data analysis results in the context of biological information on each gene available in public databases such as Entrez Gene. EVA brings a flexible combination of statistics and biological annotation to the user's desktop in a straightforward visual interface. Using a publicly available microarray dataset of gene expression response to chemotherapeutic agents in human breast cancer cell lines, we demonstrate the usefulness of the EVA system for interpreting statistical results. EVA can extend previous analyses as well as aid in making novel discoveries. Thus, we anticipate EVA will prove a useful addition to the repertoire of computational methods for microarray data analysis. The EVA software is freely available to academic users.

Antineoplastic Agents↗
PubMed2005

Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility.

Study results of single nucleotide polymorphisms (SNPs) and cancer susceptibility are often conflicting, possibly because of the analytic challenges of testing for multiple genetic and environmental risk factors using traditional analytic tools. We investigated the relationship between DNA repair gene SNPs, smoking, and bladder cancer susceptibility in 355 cases and 559 controls enrolled in a population-based study of bladder cancer in the US. Our multifaceted analytical approach included logistic regression, multifactor dimensionality reduction, and hierarchical interaction graphs for the analysis of gene-gene and gene-environment interactions followed by linkage disequilibrium and haplotype analysis. Overall, we did not find an association between any single DNA repair gene SNP and bladder cancer risk. We did find a marginally significant elevated risk of the XPD codon 751 homozygote variant among never smokers [adjusted odds ratio (OR) 2.5, 95% confidence interval (CI) 1.0-6.2]. In addition, the XRCC1 194 variant allele was associated with a reduced bladder cancer risk among heavy smokers [adjusted OR 0.4, 95% CI 0.2-0.9)]. The best predictors of bladder cancer included the XPD codon 751 and 312 SNPs along with smoking. Interpretation of this multifactor model revealed that the relationship between the XPD SNPs and bladder cancer is mostly non-additive while the effect of smoking is mostly additive. Since the two XPD SNPs are in significant linkage disequilibrium (D' = 0.52, P = 0.0001), we estimated XPD haplotypes. Individuals with variant XPD haplotypes were more susceptible to bladder cancer [e.g. adjusted OR 2.5, 95% CI 1.7-3.6] and the effect was magnified when smoking was considered. These results support the hypothesis that common polymorphisms in DNA repair genes modify bladder cancer risk and emphasize the need for a multifaceted statistical approach to identify gene-gene and gene-environment interactions.

Adult↗
PubMed2005

Proteomic-based prognosis of brain tumor patients using direct-tissue matrix-assisted laser desorption ionization mass spectrometry.

Clinical diagnosis and treatment decisions for a subset of primary human brain tumors, gliomas, are based almost exclusively on tissue histology. Approaches for glioma diagnosis can be highly subjective due to the heterogeneity and infiltrative nature of these tumors and depend on the skill of the neuropathologist. There is therefore a critical need to develop more precise, non-subjective, and systematic methods to classify human gliomas. To this end, mass spectrometric analysis has been applied to these tumors to determine glioma-specific protein patterns. Protein profiles have been obtained from human gliomas of various grades through direct analysis of tissue samples using matrix-assisted laser desorption ionization mass spectrometry (MS). Statistical algorithms applied to the MS profiles from tissue sections identified protein patterns that correlated with tumor histology and patient survival. Using a data set of 108 glioma patients, two patient populations, a short-term and a long-term survival group, were identified based on the tissue protein profiles. In addition, a subset of 57 patients diagnosed with high-grade, grade IV, malignant gliomas were analyzed and a novel classification scheme that segregated short-term and long-term survival patients based on the proteomic profiles was developed. The protein patterns described served as an independent indicator of patient survival. These results show that this new molecular approach to monitoring gliomas can provide clinically relevant information on tumor malignancy and is suitable for high-throughput clinical screening.

Biomarkers, Tumor↗
PubMed2005

A gene expression fingerprint of C. elegans embryonic motor neurons.

BACKGROUND: Differential gene expression specifies the highly diverse cell types that constitute the nervous system. With its sequenced genome and simple, well-defined neuroanatomy, the nematode C. elegans is a useful model system in which to correlate gene expression with neuron identity. The UNC-4 transcription factor is expressed in thirteen embryonic motor neurons where it specifies axonal morphology and synaptic function. These cells can be marked with an unc-4::GFP reporter transgene. Here we describe a powerful strategy, Micro-Array Profiling of C. elegans cells (MAPCeL), and confirm that this approach provides a comprehensive gene expression profile of unc-4::GFP motor neurons in vivo. RESULTS: Fluorescence Activated Cell Sorting (FACS) was used to isolate unc-4::GFP neurons from primary cultures of C. elegans embryonic cells. Microarray experiments detected 6,217 unique transcripts of which approximately 1,000 are enriched in unc-4::GFP neurons relative to the average nematode embryonic cell. The reliability of these data was validated by the detection of known cell-specific transcripts and by expression in UNC-4 motor neurons of GFP reporters derived from the enriched data set. In addition to genes involved in neurotransmitter packaging and release, the microarray data include transcripts for receptors to a remarkably wide variety of signaling molecules. The added presence of a robust array of G-protein pathway components is indicative of complex and highly integrated mechanisms for modulating motor neuron activity. Over half of the enriched genes (537) have human homologs, a finding that could reflect substantial overlap with the gene expression repertoire of mammalian motor neurons. CONCLUSION: We have described a microarray-based method, MAPCeL, for profiling gene expression in specific C. elegans motor neurons and provide evidence that this approach can reveal candidate genes for key roles in the differentiation and function of these cells. These methods can now be applied to generate a gene expression map of the C. elegans nervous system.

Animals↗
PubMed2005

Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis.

Epistasis plays an important role in the genetic architecture of common human diseases and can be viewed from two perspectives, biological and statistical, each derived from and leading to different assumptions and research strategies. Biological epistasis is the result of physical interactions among biomolecules within gene regulatory networks and biochemical pathways in an individual such that the effect of a gene on a phenotype is dependent on one or more other genes. In contrast, statistical epistasis is defined as deviation from additivity in a mathematical model summarizing the relationship between multilocus genotypes and phenotypic variation in a population. The goal of this essay is to review definitions and examples of biological and statistical epistasis and to explore the relationship between the two. Specifically, we present and discuss the following two questions in the context of human health and disease. First, when does statistical evidence of epistasis in human populations imply underlying biomolecular interactions in the etiology of disease? Second, when do biomolecular interactions produce patterns of statistical epistasis in human populations? Answers to these two reciprocal questions will provide an important framework for using genetic information to improve our ability to diagnose, prevent and treat common human diseases. We propose that systems biology will provide the necessary information for addressing these questions and that model systems such as bacteria, yeast and digital organisms will be a useful place to start.

Animals↗
PubMed2005

Combinatorial pharmacogenetics.

Combinatorial pharmacogenetics seeks to characterize genetic variations that affect reactions to potentially toxic agents within the complex metabolic networks of the human body. Polymorphic drug-metabolizing enzymes are likely to represent some of the most common inheritable risk factors associated with common 'disease' phenotypes, such as adverse drug reactions. The relatively high concordance between polymorphisms in drug-metabolizing enzymes and clinical phenotypes indicates that research into this class of polymorphisms could benefit patients in the near future. Characterization of other genes affecting drug disposition (absorption, distribution, metabolism and elimination) will further enhance this process. As with most questions concerning biological systems, the complexity arises out of the combinatorial magnitude of all the possible interactions and pathways. The high-dimensionality of the resulting analysis problem will often overwhelm traditional analysis methods. Novel analysis techniques, such as multifactor dimensionality reduction, offer viable options for evaluating such data.

Animals↗
PubMed2005

Relative impact of CYP3A genotype and concomitant medication on the severity of atorvastatin-induced muscle damage.

Atorvastatin is metabolized through enzymes encoded by members of the cytochrome P-450 (CYP) 3A gene family. Some patients who take atorvastatin along with concomitant medications known to inhibit CYP3A enzyme activity (e.g. itraconazole) develop rhabdomyolysis secondary to a severe drug-induced myopathy. The present study aimed to characterize the relationship between CYP3A gene polymorphisms and atorvastatin-induced muscle damage in the context of concomitant medication. The study employed a retrospective case--control (n=137) design. Study subjects were recruited from the general patient population served by Marshfield Clinic, a large horizontally integrated multispecialty group practice located in central Wisconsin, and case assignment was based upon both subjective (myalgia) and objective inclusion criteria [elevated serum creatine kinase (CK) levels]. The primary outcome was the relationship between serum CK level and CYP3A genotype. CYP3A genotype was not associated with an increased risk for the development of atorvastatin-induced muscle damage. CYP3A4*1B and CYP3A5*3 allele frequencies were similar in cases (n=68) and controls (n=69). Conversely, CYP3A genotype was associated with an increased severity of atorvastatin-induced muscle damage. An association was identified between the non-functional CYP3A5*3 allele and the magnitude of serum CK elevation in case patients experiencing myalgia. Patients who were homozygous for CYP3A5*3 demonstrated greater serum CK levels than patients who were heterozygous for CYP3A5*3, when concomitant lipid-lowering agents were sequentially removed from the analysis (P=0.025 without gemfibrozil, P=0.010 without gemfibrozil and niacin). The study demonstrates that patients who develop myalgia while taking atorvastatin are more likely to experience a greater degree of muscle damage if they express two copies of CYP3A5*3.

Adult↗
PubMed2005

The interaction of four genes in the inflammation pathway significantly predicts prostate cancer risk.

It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap, we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk. We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the CAPS (CAncer Prostate in Sweden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP, and TLR5) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and quickly evolving field.

Case-Control Studies↗
PubMed2005

Exploratory visual analysis of pharmacogenomic results.

Comprehensive analysis of expansive pharmacogenomic datasets is a daunting challenge. A thorough exploration of experimental results requires both statistical and annotative information. Therefore, appropriate analysis tools must bring a readily-accessible, flexible combination of statistics and biological annotation to the user's desktop. We present the Exploratory Visual Analysis (EVA) software and database as such a tool and demonstrate its utility in replicating the findings of an earlier pharmacogenomic study as well as elucidating novel biologically plausible hypotheses. EVA brings all of the often disparate pieces of analysis together in an infinitely flexible visual display that is amenable to any type of statistical result and biological question. Here, we describe the motivations for developing EVA, detail the database and custom graphical user interface (GUI), provide an example of its application to a publicly available pharmacogenomic dataset, and discuss the broad utility of the EVA tool for the pharmacogenomics community.

Computational Biology↗