Biomedical subjects

Jason H Moore

Publications and source records attributed to Jason H Moore.

At least 55 records · Page 3Linked to original sources

PubMed2004

Linear dynamic features of ambulatory blood pressure in a population-based study.

BACKGROUND: The objective of the present study was to characterize inter-individual variation in traits that measure linear dynamic features of ambulatory blood pressure (BP) measurements that may be used in future population studies to investigate the relationships between variation in genetic and environmental factors that influence BP regulation and risk of hypertension, and among hypertensives, provide new insights into variation in risk of target organ damage. DESIGN: We obtained ambulatory BP measurements every 10 min over a 24-h period in 199 healthy non-Hispanic Whites (84 females, 115 males) from Rochester, Minnesota, USA. METHODS: We then used the autocorrelation function (ACF) to measure linear dynamic features of the daytime and night-time observations in females and males separately. Re-sampling procedures were used to test whether these individuals had significant linear dependencies. RESULTS: We found that systolic and diastolic ambulatory BP measurements in our sample have statistically significant linear dependencies and that the ACFs are approximately normally distributed among individuals. In addition, we found that the distributions of the ACF traits among individuals are not significantly correlated with the mean of the ambulatory BP measurements, are different in men and women, and are different during the daytime versus the night-time. CONCLUSIONS: We conclude that ACF traits are different measures of BP than the mean that capture information about the biology of BP regulation reflected in the dynamics of a person's minute-to-minute and hour-to-hour BP phenotype. Studies of measures of linear dynamic features of BP are likely to offer new insights into the genetic and environmental factors that contribute to inter-individual variation in BP regulation and risk of future hypertension as well as new insights into the causes of variation in risk of target organ damage in individuals with established hypertension.

Blood Pressure↗

PubMed2004

Multilocus analysis of hypertension: a hierarchical approach.

While hypertension is a complex disease with a well-documented genetic component, genetic studies often fail to replicate findings. One possibility for such inconsistency is that the underlying genetics of hypertension is not based on single genes of major effect, but on interactions among genes. To test this hypothesis, we studied both single locus and multilocus effects, using a case-control design of subjects from Ghana. Thirteen polymorphisms in eight candidate genes were studied. Each candidate gene has been shown to play a physiological role in blood pressure regulation and affects one of four pathways that modulate blood pressure: vasoconstriction (angiotensinogen, angiotensin converting enzyme - ACE, angiotensin II receptor), nitric oxide (NO) dependent and NO independent vasodilation pathways and sodium balance (G protein-coupled receptor kinase, GRK4). We evaluated single site allelic and genotypic associations, multilocus genotype equilibrium and multilocus genotype associations, using multifactor dimensionality reduction (MDR). For MDR, we performed systematic reanalysis of the data to address the role of various physiological pathways. We found no significant single site associations, but the hypertensive class deviated significantly from genotype equilibrium in more than 25% of all multilocus comparisons (2,162 of 8,178), whereas the normotensive class rarely did (11 of 8,178). The MDR analysis identified a two-locus model including ACE and GRK4 that successfully predicted blood pressure phenotype 70.5% of the time. Thus, our data indicate epistatic interactions play a major role in hypertension susceptibility. Our data also support a model where multiple pathways need to be affected in order to predispose to hypertension.

Alleles↗

PubMed2004

Profiles of gene expression in human autoimmune disease.

Human autoimmune diseases arise from complex interactions between genetic and environmental factors, result from immune attack upon target tissues, and affect 3-5% of the population. We compared gene expression profiles (>4000 genes) in the peripheral blood mononuclear cells of normal individuals after immunization to individuals with four different autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus, insulin-dependent diabetes mellitus, and multiple sclerosis). All autoimmune individuals, including unaffected first-degree relatives, share a common gene expression profile that is completely distinct from the immune profile. Therefore, this expression pattern is not simply a recapitulation of the immune response to nonself, is not a result of the disease process, and results, as least in part, from genetic factors. Surprisingly, these genes are clustered in chromosomal domains suggesting there is some genome-wide logic to this unique expression pattern. These data argue that that there is a constant pattern of gene expression in autoimmunity that is independent of the specific autoimmune disease and clinical parameters associated with any individual autoimmune disease.

Autoimmune Diseases↗

PubMed2004

Computational analysis of gene-gene interactions using multifactor dimensionality reduction.

Understanding the relationship between DNA sequence variations and biologic traits is expected to improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearities in the genotype-to-phenotype mapping relationship as a result of gene-gene interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known as multifactor dimensionality reduction that was specifically designed for the identification of multilocus genetic effects is presented. Several case studies that demonstrate the detection of gene-gene interactions in common diseases such as atrial fibrillation, Type II diabetes and essential hypertension are also discussed.

Computational Biology↗

PubMed2004

Integrated analysis of genetic, genomic and proteomic data.

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.

Animals↗

PubMed2004

Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

Multifactor Dimensionality Reduction (MDR) is a method for the classification and prediction of discrete clinical endpoints using attributes constructed from multilocus genotype data. Empirical studies with both real and simulated data suggest that MDR has good power for detecting gene-gene interactions in the absence of independent main effects. The purpose of this study is to develop an objective, theory-driven approach to evaluate the strengths and limitations of MDR. To accomplish this goal, we borrow concepts from ideal observer analysis used in visual perception to evaluate the theoretical limits of classifying and predicting discrete clinical endpoints using multilocus genotype data. We conclude that MDR ideally discriminates between low risk and high risk subjects using attributes constructed from multilocus genotype data. We also how that the classification approach used once a multilocus attribute is constructed is similar to that of a naive Bayes classifier. This study provides a theoretical foundation for the continued development, evaluation, and application of the MDR as a data mining tool in the domain of statistical genetics and genetic epidemiology.

Animals↗

PubMed2003

A training-testing approach to the molecular classification of resected non-small cell lung cancer.

PURPOSE: RNA expression patterns associated with non-small cell lung cancer subclassification have been reported, but there are substantial differences in the key genes and clinical features of these subsets casting doubt on their biological significance. EXPERIMENTAL DESIGN: In this study, we used a training-testing approach to test the reliability of cDNA microarray-based classifications of resected human non-small cell lung cancers (NSCLCs) analyzed by cDNA microarray. RESULTS: Groups of genes were identified that were able to differentiate primary tumors from normal lung and lung metastases, as well as identify known histological subgroups of NSCLCs. Groups of genes were identified to discriminate sample clusters. A blinded confirmatory set of tumors was correctly classified by using these patterns. Some histologically diagnosed large cell tumors were clearly classified by expression profile analysis as being either adenocarcinoma or squamous cell carcinoma, indicating that this group of tumors may not be genetically homogeneous. High alpha-actinin-4 expression was identified as highly correlated with poor prognosis. CONCLUSIONS: These results demonstrate that gene expression profiling can identify molecular classes of resected NSCLCs that correctly classifies a blinded test cohort, and correlates with and supplements standard histological evaluation.

Actinin↗

PubMed2003

MnSOD polymorphism and breast cancer in a population-based case-control study.

A polymorphism in the signal sequence (Ala-9Val) of the gene encoding the free radical-quenching manganese superoxide dismutase (MnSOD) has been reported to alter the risk for breast cancer. We evaluated this relationship in a population-based case-control study (476 breast cancer cases and 502 controls). Overall, relative risks were not significantly elevated in women with one (RR: 1.27; 95% CI: 0.91-1.77) or two (RR: 1.18; 95% CI: 0.81-1.73) Ala alleles compared to those homozygous for the wild-type Val genotype. Results do not support any overall association of the Ala-9Val MnSOD polymorphism to the development of breast cancer.

Alanine↗

PubMed2003

Proteomic patterns of tumour subsets in non-small-cell lung cancer.

BACKGROUND: Proteomics-based approaches complement the genome initiatives and may be the next step in attempts to understand the biology of cancer. We used matrix-assisted laser desorption/ionisation mass spectrometry directly from 1-mm regions of single frozen tissue sections for profiling of protein expression from surgically resected tissues to classify lung tumours. METHODS: Proteomic spectra were obtained and aligned from 79 lung tumours and 14 normal lung tissues. We built a class-prediction model with the proteomic patterns in a training cohort of 42 lung tumours and eight normal lung samples, and assessed their statistical significance. We then applied this model to a blinded test cohort, including 37 lung tumours and six normal lung samples, to estimate the misclassification rate. FINDINGS: We obtained more than 1600 protein peaks from histologically selected 1 mm diameter regions of single frozen sections from each tissue. Class-prediction models based on differentially expressed peaks enabled us to perfectly classify lung cancer histologies, distinguish primary tumours from metastases to the lung from other sites, and classify nodal involvement with 85% accuracy in the training cohort. This model nearly perfectly classified samples in the independent blinded test cohort. We also obtained a proteomic pattern comprised of 15 distinct mass spectrometry peaks that distinguished between patients with resected non-small-cell lung cancer who had poor prognosis (median survival 6 months, n=25) and those who had good prognosis (median survival 33 months, n=41, p<0.0001). INTERPRETATION: Proteomic patterns obtained directly from small amounts of fresh frozen lung-tumour tissue could be used to accurately classify and predict histological groups as well as nodal involvement and survival in resected non-small-cell lung cancer.

Biomarkers, Tumor↗

PubMed2003

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

BACKGROUND: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. RESULTS: Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. CONCLUSION: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.

Algorithms↗

PubMed2003

Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

MOTIVATION: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. RESULTS: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. AVAILABILITY: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. SUPPLEMENTARY INFORMATION: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.

Algorithms↗

PubMed2003

Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.

The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus genotype information to improve the identification of polymorphism combinations associated with disease risk. The MDR approach is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., assumes no particular inheritance model), and is directly applicable to case-control and discordant sib-pair study designs. Both empirical and theoretical studies suggest that MDR has excellent power for identifying high-order gene-gene interactions. However, the power of MDR for identifying gene-gene interactions in the presence of common sources of noise is not currently known. The goal of this study was to evaluate the power of MDR for identifying gene-gene interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic or locus heterogeneity. Using simulated data, we show that MDR has high power to identify gene-gene interactions in the presence of 5% genotyping error, 5% missing data, or a combination of both. However, MDR has reduced power for some models in the presence of 50% phenocopy, and very limited power in the presence of 50% genetic heterogeneity. Extending MDR to address genetic heterogeneity should be a priority for the continued methodological development of this new approach.

Algorithms↗

PubMed2003

Petri net modeling of high-order genetic systems using grammatical evolution.

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

Algorithms↗

PubMed2003

Effect of cardiopulmonary bypass on urea cycle intermediates and nitric oxide levels after congenital heart surgery.

OBJECTIVE: To test the hypothesis that cardiopulmonary bypass used for repair of ventricular septal defects and atrioventricular septal defects would decrease availability of urea cycle intermediates including arginine and subsequent nitric oxide availability. STUDY DESIGN: Consecutive infants (n = 26) undergoing cardiopulmonary bypass for repair of an unrestrictive ventricular septal defect or atrioventricular septal defect were studied. Blood samples were collected immediately before surgery, immediately after surgery, and 12 hours, 24 hours, and 48 hours after surgery. Urea cycle intermediates, including citrulline, arginine, and ornithine, were measured by amino acid analysis. Nitric oxide metabolites were measured by means of the modified Griess reaction. RESULTS: Cardiopulmonary bypass caused a significant decrease in the urea cycle intermediates arginine, citrulline, and ornithine at all postoperative time points compared with preoperative levels. The ratio of ornithine to citrulline, a marker of urea cycle function, was elevated at all postoperative time points compared with preoperative values, indicating decreased urea cycle function. Nitric oxide metabolites were significantly decreased at all postoperative time points except for 48 hours, compared with preoperative levels. CONCLUSIONS: Cardiopulmonary bypass significantly decreases availability of arginine, citrulline, and nitric oxide metabolites in the postoperative period. Decreased availability of nitric oxide precursors may contribute to the increased risk of postoperative pulmonary hypertension.

Arginine↗

PubMed2003

The ubiquitous nature of epistasis in determining susceptibility to common human diseases.

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.

Disease↗

PubMed2003

Gene expression profiles in human autoimmune disease.

To acquire a functional view of human autoimmunity, we compared differences in gene expression (>4000 genes) in the peripheral blood mononuclear cells of normal individuals following immunization to those in individuals with four different autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus, insulin dependent diabetes mellitus, and multiple sclerosis). Each individual from all disease groups displayed a similar pattern of gene expression that was highly distinct from the gene expression pattern of the immunized group. These findings indicate that the expression pattern accompanying autoimmunity is not simply a recapitulation of the immune response to non-self. Of note, expression levels of genes that encode key proteins in several distinct apoptosis pathways were markedly reduced in all autoimmune disease groups. Taken together, these data indicate that the pattern of gene expression describes a molecular portrait of autoimmunity that is constant among individuals with autoimmune disease but is independent of the specific autoimmune disease and the clinical parameters associated with any individual autoimmune disease.

Animals↗

PubMed2002

Symbolic discriminant analysis of microarray data in autoimmune disease.

New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for genetic epidemiologists will be to develop statistical and computational methods that are able to identify subsets of gene expression variables that classify and predict clinical endpoints. Linear discriminant analysis is a popular multivariate statistical approach for classification of observations into groups. This is because the theory is well described and the method is easy to implement and interpret. However, an important limitation is that linear discriminant functions need to be prespecified. To address this limitation and the limitation of linearity, we have developed symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and discriminant functions that can take any form. In the present study, we demonstrate that SDA is capable of identifying combinations of gene expression variables that are able to classify and predict autoimmune diseases.

Autoimmune Diseases↗

PubMed2002

New strategies for identifying gene-gene interactions in hypertension.

Essential hypertension is a common disease that has a complex multifactorial etiology. For this reason, it is not surprising that studies of the effects of single genes on hypertension have often failed to replicate the original findings. We propose, as a working hypothesis, that the failure to replicate some single locus results is because the impact of single alleles on the risk of hypertension is dependent on genetic variations at other loci (i.e. gene-gene interactions) and on environmental factors (i.e. gene-environment interactions). Thus, studies that do not consider the appropriate genetic and/or environmental contexts may not identify important susceptibility loci. The identification and characterization of such gene-gene and gene-environment interactions have been limited by a lack of powerful statistical methods and/or a lack of large enough sample sizes. Here, we review the general problem of identifying gene-gene interactions and describe several traditional and several newer methods that are being used to assess complex genetic interactions in essential hypertension.

Gene Frequency↗