Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP).

Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP's species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.

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.

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.

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.

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

BACKGROUND: Subcortical 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. METHODS: As 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. RESULTS: Four 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%. CONCLUSION: These 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.

Multiple interactions between the alpha 2C- and beta1-adrenergic receptors influence heart failure survival.

BACKGROUND: Persistent stimulation of cardiac beta1-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 alpha 2C-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. METHODS: Sixteen 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. RESULTS: Three 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. CONCLUSION: Multiple polymorphisms act synergistically between the ADRA2C and ADRB1 genes to increase risk of death or cardiac transplant in heart failure patients.

A GRK5 polymorphism that inhibits beta-adrenergic receptor signaling is protective in heart failure.

Beta-adrenergic receptor (betaAR) blockade is a standard therapy for cardiac failure and ischemia. G protein-coupled receptor kinases (GRKs) desensitize betaARs, suggesting that genetic GRK variants might modify outcomes in these syndromes. Re-sequencing of GRK2 and GRK5 revealed a nonsynonymous polymorphism of GRK5, common in African Americans, in which leucine is substituted for glutamine at position 41. GRK5-Leu41 uncoupled isoproterenol-stimulated responses more effectively than did GRK5-Gln41 in transfected cells and transgenic mice, and, like pharmacological betaAR blockade, GRK5-Leu41 protected against experimental catecholamine-induced cardiomyopathy. Human association studies showed a pharmacogenomic interaction between GRK5-Leu41 and beta-blocker treatment, in which the presence of the GRK5-Leu41 polymorphism was associated with decreased mortality in African Americans with heart failure or cardiac ischemia. In 375 prospectively followed African-American subjects with heart failure, GRK5-Leu41 protected against death or cardiac transplantation. Enhanced betaAR desensitization of excessive catecholamine signaling by GRK5-Leu41 provides a 'genetic beta-blockade' that improves survival in African Americans with heart failure, suggesting a reason for conflicting results of beta-blocker clinical trials in this population.

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.

A functional polymorphism of the Galphaq (GNAQ) gene is associated with accelerated mortality in African-American heart failure.

Galphaq, encoded by the human GNAQ gene, is an effector subunit of the Gq heterotrimeric G-protein and the convergence point for signaling of multiple Gq-coupled neurohormonal receptors. To identify naturally occurring mutations that could modify GNAQ transcription, we examined genomic DNA isolated from 355 normal subjects for genetic variants in transcription factor binding motifs. Of seven variants identified, the most common was a GC to TT dinucleotide substitution at -694/-695 (allele frequency of 0.467 in Caucasians and 0.329 in African Americans) within a GC-rich domain containing consensus binding sites for Sp-1, c-rel and EGR-1. In promoter-reporter analyses, the TT substitution increased promoter activity in cultured neonatal rat cardiac myocytes and human HEK fibroblasts by approximately 30% at baseline and after stimulation with phorbol ester. Two other relatively common polymorphisms, -173G/A and -168G/A, did not affect promoter activity. Since altered expression/activity of Galphaq is implicated in heart disease, we re-sequenced the GNAQ promoter in 1052 prospectively followed heart failure patients. The TT variant was not increased in heart failure, but was associated with decreased survival time among African Americans, with an adjusted RR of death/cardiac transplant of 1.95 (95% CI = 1.21-3.13) for heterozygotes and 2.4 (95% CI = 1.36-4.26) for homozygotes. Gel mobility shift assays showed that this GC/TT substitution eliminated Sp-1 binding without affecting c-rel or EGR-1 binding to this promoter fragment. Thus, the GNAQ -694/-695 promoter polymorphism alters transcription factor binding, increases promoter activity and adversely affects outcome in human heart failure.

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