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

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

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

The tumor-associated YB-1 protein: new player in the circadian control of cell proliferation

Correct spatial and temporal control of cell proliferation is of fundamental importance for tissue homeostasis. Its deregulation has been associated with several pathological conditions. In common with almost every aspect of plant and animal biology, cell proliferation is dominated by day-night rhythms generated by the circadian clock. However, our understanding of the crosstalk between the core clock and cell cycle control mechanisms remains incomplete. In this study, using zebrafish as a vertebrate model system, we show that the nuclear localization of the Y-box binding protein 1 (YB-1), a regulator of cyclin expression and a hallmark of certain cancers, is robustly regulated by the circadian clock. We implicate clock-controlled changes in YB-1 SUMOylation as one of the mechanisms regulating its periodic nuclear entry at the beginning of the light phase. Furthermore, we demonstrate that YB-1 nuclear protein is able to downregulate cyclin A2 mRNA expression in zebrafish via its direct interaction with the cyclin A2 promoter. Thus, by acting as a direct target of cyclic posttranslational regulatory mechanisms, YB-1 serves as one bridge between the circadian clock and its cell cycle control

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

Familial breast cancer: characteristics and outcome of BRCA 1–2 positive and negative cases

BACKGROUND: The clinical and pathological characteristics and the clinical course of patients with breast cancer and BRCA 1–2 mutation are poorly known. METHODS: From 1997, patients with breast cancer and a family history of breast or ovarian cancer were offered BRCA testing. The clinical and pathological features of patients with known BRCA status were retrospectively assessed and comparisons were made between cancers arising in BRCA positive and BRCA wild type (WT) patients respectively. Type of treatment, pattern of relapse, event (local relapse, contralateral breast cancer, metastases) free and overall survival were also compared in the two groups. Out of the 210 patients tested, 125 had been treated and followed-up at our Institution and were evaluated in this study. RESULTS: BRCA positive patients tended to be more often premenopausal (79% vs 65%) and to have positive lymphnodes (63% vs 49%), poorly differentiated tumours (76% vs 40% – p = 0.002 at univariate analysis, not significant at multivariate analysis) and negative estrogen receptors (43% vs 29%). Treatment was not different in the two groups. In the 86 BRCA-WT patients, the first event was a local relapse in 3 (3%), metachronous contralateral breast cancer in 7 (8%) and distant metastases in 16 (19%). In the 39 BRCA positive patients, the corresponding figures were 3 (8%), 8 (21%) and 3 (8%). There was no difference in event free survival, with a median of 180 months in both groups of patients. At 20 years, projected survival was 85% for BRCA positive patients and 55% for BRCA-WT, but this difference was not statistically significant. CONCLUSION: Although BRCA positive patients have more frequently negative prognostic factors, their prognosis appears to be equal to or better than in patients with BRCA-WT

Effect of Prior Bilateral Oophorectomy on the Presentation of Breast Cancer in BRCA1 and BRCA2 Mutation Carriers

Purpose: To compare the presentation of invasive breast cancer in BRCA1 and BRCA2 mutation carriers with and without prior bilateral oophorectomy. Patients and methods: Women with a BRCA1 or BRCA2 mutation with the diagnosis of invasive breast cancer were identified from ten cancer genetics clinics. The medical history, medical treatment records and pathology reports for the breast cancers were reviewed. Information was abstracted from medical charts, including history (and date) of oophorectomy, date of breast cancer diagnosis, stage of disease, and pathologic characteristics of the breast cancer. Women with prior bilateral oophorectomy were matched by age, year of diagnosis, and mutation with one or more women who had two intact ovaries at the time of breast cancer diagnosis. Characteristics of the breast tumours were compared between the two groups

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