Reagan Kelly

Clinical and Genetic Modifiers of Long-Term Survival in Heart Failure

OBJECTIVES This study sought to identify genetic modifiers of beta-blocker response and long-term survival in heart failure (HF). BACKGROUND Differences in beta-blocker treatment effect between Caucasians and African Americans with HF have been reported. METHODS This was a prospective cohort study of 2,460 patients (711 African American, 1,749 Caucasian) enrolled between 1999 and 2007; 2,039 patients (81.7%) were treated with a beta-blocker. Each was genotyped for beta1-adrenergic receptor (ADRB1) Arg389>Gly and G-protein receptor kinase 5 (GRK5) Gln41>Leu polymorphisms, which are more prevalent among African Americans than Caucasians. The primary end point was survival time from HF onset. RESULTS There were 765 deaths during follow-up (median 46 months). beta-blocker treatment increased survival in Caucasians (log-rank p = 0.00038) but not African Americans (log-rank p = 0.327). Among patients not taking beta-blockers, ADRB1 Gly389 was associated with decreased survival in Caucasians (hazard ratio [HR]: 1.98, 95% confidence interval [CI]: 1.1 to 3.7, p = 0.03) whereas GRK5 Leu41 was associated with improved survival in African Americans (HR: 0.325, CI: 0.133 to 0.796, p = 0.01). African Americans with ADRB1 Gly389Gly GRK5 Gln41Gln derived a similar survival benefit from beta-blocker therapy (HR: 0.385, 95% CI: 0.182 to 0.813, p = 0.012) as Caucasians with the same genotype (HR: 0.529, 95% CI: 0.326 to 0.858, p = 0.0098). CONCLUSIONS These data show that differences caused by beta-adrenergic receptor signaling pathway gene polymorphisms, rather than race, are the major factors contributing to apparent differences in the beta-blocker treatment effect between Caucasians and African Americans; proper evaluation of treatment response should account for genetic variance.

Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.

Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administration-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR models performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the models applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compounds risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

The genetic architecture of fasting plasma triglyceride response to fenofibrate treatment

Metabolic response to the triglyceride (TG)-lowering drug, fenofibrate, is shaped by interactions between genetic and environmental factors, yet knowledge regarding the genetic determinants of this response is primarily limited to single-gene effects. Since very low-density lipoprotein (VLDL) is the central carrier of fasting TG, identifying factors that affect both total TG and VLDL–TG response to fenofibrate is critical for predicting individual fenofibrate response. As part of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, 688 individuals from 161 families were genotyped for 91 single-nucleotide polymorphisms (SNPs) in 25 genes known to be involved in lipoprotein metabolism. Using generalized estimating equations to control for family structure, we performed linear modeling to investigate whether single SNPs, single covariates, SNP–SNP interactions, and/or SNP–covariate interactions had a significant association with the change in total fasting TG and fasting VLDL–TG after 3 weeks of fenofibrate treatment. A 10-iteration fourfold cross-validation procedure was used to validate significant associations and quantify their predictive abilities. More than one-third of the significant, cross-validated SNP–SNP interactions predicting each outcome involved just five SNPs, showing that these SNPs are of key importance to fenofibrate response. Multiple variable models constructed using the top-ranked SNP--covariate interactions explained 11.9% more variation in the change in TG and 7.8% more variation in the change in VLDL than baseline TG alone. These results yield insight into the complex biology of fenofibrate response, which can be used to target fenofibrate therapy to individuals who are most likely to benefit from the drug.

Hypertrophy-associated polymorphisms ascertained in a founder cohort applied to heart failure risk and mortality.

A three‐stage approach was undertaken using genome‐wide, case‐control, and case‐only association studies to identify genetic variants associated with heart failure mortality. In an Amish founder population (n = 851), cardiac hypertrophy, a trait integral to the adaptive response to failure, was found to be heritable (h2= 0.28, p = 0.0002) and GWAS revealed 21 candidate hypertrophy SNPs. In a case (n = 1,610)‐control (n = 463) study in unrelated Caucasians, one of the SNPs associated with hypertrophy (rs2207418, p = 8 × 10−6), was associated with heart failure, RR = 1.85(1.25–2.73, p = 0.0019). In heart failure cases rs2207418 was associated with increased mortality, HR = 1.51(1.20–1.97, p = 0.0004). There was consistency between studies, with the GG allele being associated with increased ventricular mass (˜13 g/m2) in the Amish, heart failure risk, and heart failure mortality. This SNP is in a gene desert of chromosome 20p12. Five genes are within 2.0 mbp of rs2207418 but with low LD between their SNPs and rs2207418. A region near this SNP is highly conserved in multiple vertebrates (lod score = 1,208). This conservation and the internal consistency across studies suggests that this region has biologic importance in heart failure, potentially acting as an enhancer or repressor element. rs2207418 may be useful for predicting a more progressive form of heart failure that may require aggressive therapy. Clin Trans Sci 2011; Volume 4: 17–23

Multiple interactions between the alpha2C- and beta1-adrenergic receptors influence heart failure survival

BackgroundPersistent stimulation of cardiac β1-adrenergic receptors by endogenous norepinephrine promotes heart failure progression. Polymorphisms of this gene are known to alter receptor function or expression, as are polymorphisms of the α2C-adrenergic receptor, which regulates norepinephrine release from cardiac presynaptic nerves. The purpose of this study was to investigate possible synergistic effects of polymorphisms of these two intronless genes (ADRB1 and ADRA2C, respectively) on the risk of death/transplant in heart failure patients.MethodsSixteen sequence variations in ADRA2C and 17 sequence variations in ADRB1 were genotyped in a longitudinal study of 655 white heart failure patients. Eleven sequence variations in each gene were polymorphic in the heart failure cohort. Cox proportional hazards modeling was used to identify polymorphisms and potential intra- or intergenic interactions that influenced risk of death or cardiac transplant. A leave-one-out cross-validation method was utilized for internal validation.ResultsThree polymorphisms in ADRA2C and five polymorphisms in ADRB1 were involved in eight cross-validated epistatic interactions identifying several two-locus genotype classes with significant relative risks ranging from 3.02 to 9.23. There was no evidence of intragenic epistasis. Combining high risk genotype classes across epistatic pairs to take into account linkage disequilibrium, the relative risk of death or transplant was 3.35 (1.82, 6.18) relative to all other genotype classes.ConclusionMultiple polymorphisms act synergistically between the ADRA2C and ADRB1 genes to increase risk of death or cardiac transplant in heart failure patients.

Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study

BackgroundSubcortical white matter hyperintensity on magnetic resonance imaging (MRI) of the brain, referred to as leukoaraiosis, is associated with increased risk of stroke and dementia. Hypertension may contribute to leukoaraiosis by accelerating the process of arteriosclerosis involving penetrating small arteries and arterioles in the brain. Leukoaraiosis volume is highly heritable but shows significant inter-individual variability that is not predicted well by any clinical covariates (except for age) or by single SNPs.MethodsAs part of the Genetics of Microangiopathic Brain Injury (GMBI) Study, 777 individuals (74% hypertensive) underwent brain MRI and were genotyped for 1649 SNPs from genes known or hypothesized to be involved in arteriosclerosis and related pathways. We examined SNP main effects, epistatic (gene-gene) interactions, and context-dependent (gene-environment) interactions between these SNPs and covariates (including conventional and novel risk factors for arteriosclerosis) for association with leukoaraiosis volume. Three methods were used to reduce the chance of false positive associations: 1) false discovery rate (FDR) adjustment for multiple testing, 2) an internal replication design, and 3) a ten-iteration four-fold cross-validation scheme.ResultsFour SNP main effects (in F3, KITLG, CAPN10, and MMP2), 12 SNP-covariate interactions (including interactions between KITLG and homocysteine, and between TGFB3 and both physical activity and C-reactive protein), and 173 SNP-SNP interactions were significant, replicated, and cross-validated. While a model containing the top single SNPs with main effects predicted only 3.72% of variation in leukoaraiosis in independent test samples, a multiple variable model that included the four most highly predictive SNP-SNP and SNP-covariate interactions predicted 11.83%.ConclusionThese results indicate that the genetic architecture of leukoaraiosis is complex, yet predictive, when the contributions of SNP main effects are considered in combination with effects of SNP interactions with other genes and covariates.

KGraph: a system for visualizing and evaluating complex genetic associations

UNLABELLED The KGraph is a data visualization system that has been developed to display the complex relationships between the univariate and bivariate associations among an outcome of interest, a set of covariates, and a set of genetic factors, such as single nucleotide polymorphisms (SNPs). It allows for easy viewing and interpretation of genetic associations, correlations among covariates and SNPs, and information about the replication and cross-validation of the associations. The KGraph allows the user to more easily investigate multicollinearity and confounding through visualization of the multidimensional correlation structure underlying genetic associations. It emphasizes gene-environment and gene-gene interaction, both important components of any genetic system that are often overlooked in association frameworks. AVAILABILITY http://www.epidkardia.sph.umich.edu/software/kgrapher

Of text and gene – using text mining methods to uncover hidden knowledge in toxicogenomics

BackgroundToxicogenomics studies often profile gene expression from assays involving multiple doses and time points. The dose- and time-dependent pattern is of great importance to assess toxicity but computational approaches are lacking to effectively utilize this characteristic in toxicity assessment. Topic modeling is a text mining approach, but may be used analogously in toxicogenomics due to the similar data structures between text and gene dysregulation.ResultsTopic modeling was applied to a very large toxicogenomics dataset containing microarray gene expression data from >15,000 samples associated with 131 drugs tested in three different assay platforms (i.e., in vitro assay, in vivo repeated dose study and in vivo single dose experiment) with a design including multiple doses and time points. A set of “topics” which each consist of a set of genes was determined, by which the varying sensitivity of three assay systems was observed. We found that the drug-dependent effect was more pronounced in the two in vivo systems than the in vitro system, while the time-dependent effect was most strongly reflected in the in vitro system followed by the single dose study and lastly the repeated dose experiment. The dose-dependent effect was similar across three assay systems. Although the results indicated a challenge to extrapolate the in vitro results to the in vivo situation, we did notice that, for some drugs but not for all the drugs, the similarity in gene expression patterns was observed across all three assay systems, indicating a possibility of using in vitro systems with careful designs (such as the choice of dose and time point), to replace the in vivo testing strategy. Nonetheless, a potential to replace the repeated dose study by the single-dose short-term methodology was strongly implied.ConclusionsThe study demonstrated that text mining methodologies such as topic modeling provide an alternative method compared to traditional means for data reduction in toxicogenomics, enhancing researchers’ capabilities to interpret biological information.

Omics Biomarkers in Risk Assessment: A Bioinformatics Perspective

Current advances in genomics, proteomics, and metabolomics are widely anticipated to translate in the future to a constellation of benefits in human health. However, few biomarkers for risk assessment using “omics” technologies have been reported in the last decade. Nevertheless, the potential application for omics technologies is tremendous. The use of biomarker-based monitoring approaches as a tool for environmental risk assessment is often critically limited by a lack of integrated bioinformatics approaches, statistical analyses, and predictive models. In this chapter we discuss the key steps for omics biomarker discovery and also present the use of the decision forest (DF) classification method as an example with specific application to microarray gene expression data, proteomics, and SNP genotypic data. An integrated bioinformatics approach with the correct choice of samples, omics technologies, and statistical techniques will allow the development of powerful new biomarkers for safety assessment.

Providing context and interpretability to genetic association analysis results using the KGraph.

The KGraph is a data visualization system that has been developed to display the complex relationships between the univariate and bivariate associations among an outcome of interest, a set of covariates, and a set of genetic variations such as single-nucleotide polymorphisms (SNPs). It allows for easy simultaneous viewing and interpretation of genetic associations, correlations among covariates and SNPs, and information about the replication and cross-validation of these associations. The KGraph allows the user to more easily investigate multicollinearity and confounding through visualization of the multidimensional correlation structure underlying genetic associations. It emphasizes gene-environment interactions, gene-gene interactions, and correlations, all important components of the complex genetic architecture of most human traits. The KGraph was designed for use in gene-centric studies, but can be integrated into association analysis workflows as well. The software is available at http://www.epidkardia.sph.umich.edu/software/kgrapher.