MixMAP: An R Package for Mixed Modeling of Meta-Analysis p Values in Genetic Association Studies

Genetic association studies are commonly conducted to identify genes that explain the variability in a measured trait (e.g., disease status or disease progression). Often, results of these studies are summarized in the form of a p value corresponding to a test of association between each single nucleotide polymorphisms (SNPs) and the trait under study. As genes are comprised of multiple SNPs, post hoc approaches are generally applied to determine gene-level association. For example, if any SNP within a gene is significantly associated with the trait at a genome-wide significance level (p < 5 x 10e-8), then the corresponding gene is considered significant. A complementary strategy, termed mix ed modeling of meta-analysis p values (MixMAP) was proposed recently to characterize formally the associations between genes (or gene regions) and a trait based on multiple SNP-level p values. Here, the MixMAP package is presented as a means for implementing the MixMAP procedure in R

Prediction-based classification for longitudinal biomarkers

Assessment of circulating CD4 count change over time in HIV-infected subjects on antiretroviral therapy (ART) is a central component of disease monitoring. The increasing number of HIV-infected subjects starting therapy and the limited capacity to support CD4 count testing within resource-limited settings have fueled interest in identifying correlates of CD4 count change such as total lymphocyte count, among others. The application of modeling techniques will be essential to this endeavor due to the typically nonlinear CD4 trajectory over time and the multiple input variables necessary for capturing CD4 variability. We propose a prediction-based classification approach that involves first stage modeling and subsequent classification based on clinically meaningful thresholds. This approach draws on existing analytical methods described in the receiver operating characteristic curve literature while presenting an extension for handling a continuous outcome. Application of this method to an independent test sample results in greater than 98% positive predictive value for CD4 count change. The prediction algorithm is derived based on a cohort of $n=270$ HIV-1 infected individuals from the Royal Free Hospital, London who were followed for up to three years from initiation of ART. A test sample comprised of $n=72$ individuals from Philadelphia and followed for a similar length of time is used for validation. Results suggest that this approach may be a useful tool for prioritizing limited laboratory resources for CD4 testing after subjects start antiretroviral therapy.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS326 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian variable selection for high dimensional predictors and self-reported outcomes

BACKGROUND: The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error. METHODS: We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women\u27s Health Initiative SNP Health Association Resource, which includes extensive genotypic ( \u3e 900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women. RESULTS: Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women\u27s Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement. CONCLUSIONS: Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports

A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci

Background Characterizing the genetic determinants of complex diseases can be further augmented by incorporating knowledge of underlying structure or classifications of the genome, such as newly developed mappings of protein-coding genes, epigenetic marks, enhancer elements and non-coding RNAs. Methods We apply a simple class-level testing framework, termed Genetic Class Association Testing (GenCAT), to identify protein-coding gene association with 14 cardiometabolic (CMD) related traits across 6 publicly available genome wide association (GWA) meta-analysis data resources. GenCAT uses SNP-level meta-analysis test statistics across all SNPs within a class of elements, as well as the size of the class and its unique correlation structure, to determine if the class is statistically meaningful. The novelty of findings is evaluated through investigation of regional signals. A subset of findings are validated using recently updated, larger meta-analysis resources. A simulation study is presented to characterize overall performance with respect to power, control of family-wise error and computational efficiency. All analysis is performed using the GenCAT package, R version 3.2.1. Results We demonstrate that class-level testing complements the common first stage minP approach that involves individual SNP-level testing followed by post-hoc ascribing of statistically significant SNPs to genes and loci. GenCAT suggests 54 protein-coding genes at 41 distinct loci for the 13 CMD traits investigated in the discovery analysis, that are beyond the discoveries of minP alone. An additional application to biological pathways demonstrates flexibility in defining genetic classes. Conclusions We conclude that it would be prudent to include class-level testing as standard practice in GWA analysis. GenCAT, for example, can be used as a simple, complementary and efficient strategy for class-level testing that leverages existing data resources, requires only summary level data in the form of test statistics, and adds significant value with respect to its potential for identifying multiple novel and clinically relevant trait associations

A likelihood-based approach to mixed modeling with ambiguity in cluster identifiers

This manuscript describes a novel, linear mixed-effects model–fitting technique for the setting in which correlated data indicators are not completely observed. Mixed modeling is a useful analytical tool for characterizing genotype–phenotype associations among multiple potentially informative genetic loci. This approach involves grouping individuals into genetic clusters, where individuals in the same cluster have similar or identical multilocus genotypes. In haplotype-based investigations of unrelated individuals, corresponding cluster assignments are unobservable since the alignment of alleles within chromosomal copies is not generally observed. We derive an expectation conditional maximization approach to estimation in the mixed modeling setting, where cluster assignments are ambiguous. The approach has broad relevance to the analysis of data with missing correlated data identifiers. An example is provided based on data arising from a cohort of human immunodeficiency virus type-1–infected individuals at risk for antiretroviral therapy–associated dyslipidemia

Associations among Race/Ethnicity, ApoC-III Genotypes, and Lipids in HIV-1-Infected Individuals on Antiretroviral Therapy

BACKGROUND: Protease inhibitors (PIs) are associated with hypertriglyceridemia and atherogenic dyslipidemia. Identifying HIV-1-infected individuals who are at increased risk of PI-related dyslipidemia will facilitate therapeutic choices that maintain viral suppression while reducing risk of atherosclerotic diseases. Apolipoprotein C-III (apoC-III) gene variants, which vary by race/ethnicity, have been associated with a lipid profile that resembles PI-induced dyslipidemia. However, the association of race/ethnicity, or candidate gene effects across race/ethnicity, with plasma lipid levels in HIV-1-infected individuals, has not been reported. METHODS AND FINDINGS: A cross-sectional analysis of race/ethnicity, apoC-III/apoA-I genotypes, and PI exposure on plasma lipids was performed in AIDS Clinical Trial Group studies (n = 626). Race/ethnicity was a highly significant predictor of plasma lipids in fully adjusted models. Furthermore, in stratified analyses, the effect of PI exposure appeared to differ across race/ethnicity. Black/non-Hispanic, compared with White/non-Hispanics and Hispanics, had lower plasma triglyceride (TG) levels overall, but the greatest increase in TG levels when exposed to PIs. In Hispanics, current PI antiretroviral therapy (ART) exposure was associated with a significantly smaller increase in TGs among patients with variant alleles at apoC-III-482, −455, and Intron 1, or at a composite apoC-III genotype, compared with patients with the wild-type genotypes. CONCLUSIONS: In the first pharmacogenetic study of its kind in HIV-1 disease, we found race/ethnic-specific differences in plasma lipid levels on ART, as well as differences in the influence of the apoC-III gene on the development of PI-related hypertriglyceridemia. Given the multi-ethnic distribution of HIV-1 infection, our findings underscore the need for future studies of metabolic and cardiovascular complications of ART that specifically account for racial/ethnic heterogeneity, particularly when assessing candidate gene effects

Low-Cost HIV-1 Diagnosis and Quantification in Dried Blood Spots by Real Time PCR

BACKGROUND: Rapid and cost-effective methods for HIV-1 diagnosis and viral load monitoring would greatly enhance the clinical management of HIV-1 infected adults and children in limited-resource settings. Recent recommendations to treat perinatally infected infants within the first year of life are feasible only if early diagnosis is routinely available. Dried blood spots (DBS) on filter paper are an easy and convenient way to collect and transport blood samples. A rapid and cost effective method to diagnose and quantify HIV-1 from DBS is urgently needed to facilitate early diagnosis of HIV-1 infection and monitoring of antiretroviral therapy. METHODS AND FINDINGS: We have developed a real-time LightCycler (rtLC) PCR assay to detect and quantify HIV-1 from DBS. HIV-1 RNA extracted from DBS was amplified in a one-step, single-tube system using primers specific for long-terminal repeat sequences that are conserved across all HIV-1 clades. SYBR Green dye was used to quantify PCR amplicons and HIV-1 RNA copy numbers were determined from a standard curve generated using serially diluted known copies of HIV-1 RNA. This assay detected samples across clades, has a dynamic range of 5 log(10), and %CV <8% up to 4 log(10) dilution. Plasma HIV-1 RNA copy numbers obtained using this method correlated well with the Roche Ultrasensitive (r = 0.91) and branched DNA (r = 0.89) assays. The lower limit of detection (95%) was estimated to be 136 copies. The rtLC DBS assay was 2.5 fold rapid as well as 40-fold cheaper when compared to commercial assays. Adaptation of the assay into other real-time systems demonstrated similar performance. CONCLUSIONS: The accuracy, reliability, genotype inclusivity and affordability, along with the small volumes of blood required for the assay suggest that the rtLC DBS assay will be useful for early diagnosis and monitoring of pediatric HIV-1 infection in resource-limited settings

Randomized Trial of Time-Limited Interruptions of Protease Inhibitor-Based Antiretroviral Therapy (ART) vs. Continuous Therapy for HIV-1 Infection

Background The clinical outcomes of short interruptions of PI-based ART regimens remains undefined. Methods A 2-arm non-inferiority trial was conducted on 53 HIV-1 infected South African participants with viral load/ml and CD4 T cell count \u3e450 cells/µl on stavudine (or zidovudine), lamivudine and lopinavir/ritonavir. Subjects were randomized to a) sequential 2, 4 and 8-week ART interruptions or b) continuous ART (cART). Primary analysis was based on the proportion of CD4 count \u3e350 cells(c)/ml over 72 weeks. Adherence, HIV-1 drug resistance, and CD4 count rise over time were analyzed as secondary endpoints. Results The proportions of CD4 counts \u3e350 cells/µl were 82.12% for the intermittent arm and 93.73 for the cART arm; the difference of 11.95% was above the defined 10% threshold for non-inferiority (upper limit of 97.5% CI, 24.1%; 2-sided CI: −0.16, 23.1). No clinically significant differences in opportunistic infections, adverse events, adherence or viral resistance were noted; after randomization, long-term CD4 rise was observed only in the cART arm. Conclusion We are unable to conclude that short PI-based ART interruptions are non-inferior to cART in retention of immune reconstitution; however, short interruptions did not lead to a greater rate of resistance mutations or adverse events than cART suggesting that this regimen may be more forgiving than NNRTIs if interruptions in therapy occur

Rapid progression of prostate cancer in men with a BRCA2 mutation.

Men with BRCA2 mutations have been found to be at increased risk of developing prostate cancer. There is a recent report that BRCA2 carriers with prostate cancer have poorer survival than noncarrier prostate cancer patients. In this study, we compared survival of men with a BRCA2 mutation and prostate cancer with that of men with a BRCA1 mutation and prostate cancer. We obtained the age at diagnosis, age at death or current age from 182 men with prostate cancer from families with a BRCA2 mutation and from 119 men with prostate cancer from families with a BRCA1 mutation. The median survival from diagnosis was 4.0 years for men with a BRCA2 mutation vs 8.0 years for men with a BRCA1 mutation, and the difference was highly significant (P&lt;0.01). It may be important to develop targeted chemotherapies to treat prostate cancer in men with a BRCA2 mutation

YB-1 recruitment to stress granules in zebrafish cells reveals a differential adaptive response to stress

The survival of cells exposed to adverse environmental conditions entails various alterations in cellular function including major changes in the transcriptome as well as a radical reprogramming of protein translation. While in mammals this process has been extensively studied, stress responses in non-mammalian vertebrates remain poorly understood. One of the key cellular responses to many different types of stressors is the transient generation of structures called stress granules (SGs). These represent cytoplasmic foci where untranslated mRNAs are sorted or processed for re-initiation, degradation, or packaging into mRNPs. Here, using the evolutionarily conserved Y-box binding protein 1 (YB-1) and G3BP1 as markers, we have studied the formation of stress granules in zebrafish (D. rerio) in response to different environmental stressors. We show that following heat shock, zebrafish cells, like mammalian cells, form stress granules which contain both YB-1 and G3BP1 proteins. Moreover, zfYB-1 knockdown compromises cell viability, as well as recruitment of G3BP1 into SGs, under heat shock conditions highlighting the essential role played by YB-1 in SG assembly and cell survival. However, zebrafish PAC2 cells do not assemble YB-1-positive stress granules upon oxidative stress induced by arsenite, copper or hydrogen peroxide treatment. This contrasts with the situation in human cells where SG formation is robustly induced by exposure to oxidative stressors. Thus, our findings point to fundamental differences in the mechanisms whereby mammalian and zebrafish cells respond to oxidative stress