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Jason H Moore

Publications and source records attributed to Jason H Moore.

At least 37 records · Page 2Linked to original sources
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↗
PubMed2004

Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics.

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 or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.

Computational Biology↗
PubMed2004

Activation of cryptic 3' splice sites within introns of cellular genes following gene entrapment.

Gene trap vectors developed for genome-wide mutagenesis can be used to study factors governing the expression of exons inserted throughout the genome. For example, entrapment vectors consisting of a partial 3'-terminal exon [i.e. a neomycin resistance gene (Neo), a poly(A) site, but no 3' splice site] were typically expressed following insertion into introns, from cellular transcripts that spliced to cryptic 3' splice sites present either within the targeting vector or in the adjacent intron. A vector (U3NeoSV1) containing the wild-type Neo sequence preferentially disrupted genes that spliced in-frame to a cryptic 3' splice site in the Neo coding sequence and expressed functional neomycin phosphotransferase fusion proteins. Removal of the cryptic Neo 3' splice site did not reduce the proportion of clones with inserts in introns; rather, the fusion transcripts utilized cryptic 3' splice sites present in the adjacent intron or generated by virus integration. However, gene entrapment with U3NeoSV2 was considerably more random than with U3NeoSV1, consistent with the widespread occurrence of potential 3' splice site sequences in the introns of cellular genes. These results clarify the mechanisms of gene entrapment by U3 gene trap vectors and illustrate features of exon definition required for 3' processing and polyadenylation of cellular transcripts.

Animals↗
PubMed2004

An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation.

BACKGROUND: To examine interactions among the angiotensin converting enzyme (ACE) insertion/deletion, plasminogen activator inhibitor-1 (PAI-1) 4G/5G, and tissue plasminogen activator (t-PA) insertion/deletion gene polymorphisms on risk of myocardial infarction using data from 343 matched case-control pairs from the Physicians Health Study. We examined the data using both conditional logistic regression and the multifactor dimensionality reduction (MDR) method. One advantage of the MDR method is that it provides an internal prediction error for validation. We summarize our use of this internal prediction error for model validation. RESULTS: The overall results for the two methods were consistent, with both suggesting an interaction between the ACE I/D and PAI-1 4G/5G polymorphisms. However, using ten-fold cross validation, the 46% prediction error for the final MDR model was not significantly lower than that expected by chance. CONCLUSIONS: The significant interaction initially observed does not validate and may represent a type I error. As data-driven analytic methods continue to be developed and used to examine complex genetic interactions, it will become increasingly important to stress model validation in order to ensure that significant effects represent true relationships rather than chance findings.

Gene Expression Regulation↗
PubMed2004

Gene expression signatures for autoimmune disease in peripheral blood mononuclear cells.

The relatively new technology of DNA microarrays offers the possibility to probe the human genome for clues to the pathogenesis and treatment of human disease. While early studies using this approach were largely in oncology, many new reports are emerging in other fields including infectious diseases and pharmacology, and applications in autoimmunity have been recently reported by our group and others. Some of these investigations have examined animal models of autoimmune disease, but a number of human studies have also been carried out. Of special interest are those that have used peripheral blood samples because, unlike tissue biopsies, these are readily available from all subjects. Using this approach, patterns of gene expression can be detected that distinguish patients with autoimmune conditions from normal subjects. Furthermore, the genes that are identified provide clues to possible pathogenetic mechanisms and are likely to be useful in developing tests to establish diagnostic categories and predict therapeutic responses.

Animals↗
PubMed2004

A high-density admixture map for disease gene discovery in african americans.

Admixture mapping (also known as "mapping by admixture linkage disequilibrium," or MALD) provides a way of localizing genes that cause disease, in admixed ethnic groups such as African Americans, with approximately 100 times fewer markers than are required for whole-genome haplotype scans. However, it has not been possible to perform powerful scans with admixture mapping because the method requires a dense map of validated markers known to have large frequency differences between Europeans and Africans. To create such a map, we screened through databases containing approximately 450000 single-nucleotide polymorphisms (SNPs) for which frequencies had been estimated in African and European population samples. We experimentally confirmed the frequencies of the most promising SNPs in a multiethnic panel of unrelated samples and identified 3011 as a MALD map (1.2 cM average spacing). We estimate that this map is approximately 70% informative in differentiating African versus European origins of chromosomal segments. This map provides a practical and powerful tool, which is freely available without restriction, for screening for disease genes in African American patient cohorts. The map is especially appropriate for those diseases that differ in incidence between the parental African and European populations.

Black or African American↗
PubMed2004

Renin-angiotensin system gene polymorphisms and atrial fibrillation.

BACKGROUND: The activated local atrial renin-angiotensin system (RAS) has been reported to play an important role in the pathogenesis of atrial fibrillation (AF). We hypothesized that RAS genes might be among the susceptibility genes of nonfamilial structural AF and conducted a genetic case-control study to demonstrate this. METHODS AND RESULTS: A total of 250 patients with documented nonfamilial structural AF and 250 controls were selected. The controls were matched to cases on a 1-to-1 basis with regard to age, gender, presence of left ventricular dysfunction, and presence of significant valvular heart disease. The ACE gene insertion/deletion polymorphism, the T174M, M235T, G-6A, A-20C, G-152A, and G-217A polymorphisms of the angiotensinogen gene, and the A1166C polymorphism of the angiotensin II type I receptor gene were genotyped. In multilocus haplotype analysis, the angiotensinogen gene haplotype profile was significantly different between cases and controls (chi2=62.5, P=0.0002). In single-locus analysis, M235T, G-6A, and G-217A were significantly associated with AF. Frequencies of the M235, G-6, and G-217 alleles were significantly higher in cases than in controls (P=0.000, 0.005, and 0.002, respectively). The odds ratios for AF were 2.5 (95% CI 1.7 to 3.3) with M235/M235 plus M235/T235 genotype, 3.3 (95% CI 1.3 to 10.0) with G-6/G-6 genotype, and 2.0 (95% CI 1.3 to 2.5) with G-217/G-217 genotype. Furthermore, significant gene-gene interactions were detected by the multifactor-dimensionality reduction method and multilocus linkage disequilibrium tests. CONCLUSIONS: This study demonstrates the association of RAS gene polymorphisms with nonfamilial structural AF and may provide the rationale for clinical trials to investigate the use of ACE inhibitor or angiotensin II antagonist in the treatment of structural AF.

Aged↗
PubMed2004

Association of homozygous wild-type glutathione S-transferase M1 genotype with increased breast cancer risk.

More than 500 studies have examined the association of the glutathione S-transferase M1 (GSTM1) genotype with various malignancies yielding inconsistent results. The genotyping was based on a PCR assay that identified the GSTM1 null (-/-) genotype but did not distinguish homozygous wild-type (+/+) and heterozygous (+/-) individuals. We developed an assay that allowed the definition of +/+, +/-, and -/- genotypes by separate identification of wild-type and null alleles, which were found with frequencies of 0.225 and 0.775, respectively, in Caucasian women. We applied the new assay to a breast cancer case-control study and identified the +/+ genotype in 14 (6.9%) of 202 control subjects compared with 37 (18.2%) of 203 patients. Compared with women with the -/- genotype, the relative risk of breast cancer for the +/+ genotype was 2.83 (95% confidence interval, 1.45-5.59; P = 0.002), suggesting a protective effect of the GSTM1 deletion.

Breast Neoplasms↗
PubMed2004

The use of animal models in the study of complex disease: all else is never equal or why do so many human studies fail to replicate animal findings?

The study of the genetics of complex human disease has met with limited success. Many findings with candidate genes fail to replicate despite seemingly overwhelming physiological data implicating the genes. In contrast, animal model studies of the same genes and disease models usually have more consistent results. We propose that one important reason for this is the ability to control genetic background in animal studies. The fact that controlling genetic background can produce more consistent results suggests that the failure to replicate human findings in the same diseases is due to variation in interacting genes. Hence, the contrasting nature of the findings from the different study designs indicates the importance of non-additive genetic effects on human disease. We discuss these issues and some methodological approaches that can detect multilocus effects, using hypertension as a model disease. This article contains supplementary material, which may be viewed at the BioEssays website at http://www.interscience.wiley.com/jpages/0265-9247/suppmat/index.html.

Animals↗
PubMed2004

Co-localization of differentially expressed genes and shared susceptibility loci in human autoimmunity.

Autoimmune diseases arise from complex interactions between environmental and genetic factors. Genetic linkage scans show that different autoimmune diseases share overlapping susceptibility loci. Lymphocytes from individuals with different autoimmune diseases, as well as unaffected first-degree relatives, also share a common gene expression profile. We sought to determine if genes within this autoimmune expression profile were nonrandomly distributed in the genome and if their distribution overlapped with shared disease susceptibility loci. We found that differentially expressed genes were distributed in a nonrandom fashion in chromosomal domains within the genome. Furthermore, positions of these domains were not statistically different from a number of shared autoimmune disease susceptibility loci. To our knowledge, this is the first study showing concordance between gene expression and genetic linkage results in common complex multifactorial human diseases.

Adult↗
PubMed2004

Genetics, statistics and human disease: analytical retooling for complexity.

Molecular biologists and geneticists alike now acknowledge that most common human diseases with a genetic component are likely to have complex etiologies. Yet despite this belief, many statistical geneticists continue applying, in slightly new and different ways, methodologies that were developed to dissect much simpler etiologies. In this article, we characterize, with examples, the various factors that can complicate genetic analysis and demonstrate their shared features and how they affect genetic analysis. We describe a variety of approaches that are currently available, revealing methodological gaps and suggesting new directions for method development. Finally, we propose a comprehensive two-step approach to analysis that systemically addresses the different genetic factors that are likely to underlie complex diseases.

Genetic Diseases, Inborn↗