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Mount Airy, MD, United States

Qu L.,Biostat Solutions Inc. | Nettleton D.,Iowa State University | Dekkers J.C.M.,Iowa State University
Biometrics | Year: 2012

For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two-component mixture model has been widely used in practice to detect differentially expressed genes. However, it naïvely imposes strong exchangeability assumptions across genes and does not make active use of a priori information about intergene relationships that is currently available, e.g., gene annotations through the Gene Ontology (GO) project. We propose a general strategy that first generates a set of covariates that summarizes the intergene information and then extends the two-component mixture model into a hierarchical semiparametric model utilizing the generated covariates through latent nonparametric regression. Simulations and analysis of real microarray data show that our method can outperform the naïve two-component mixture model. © 2012, The International Biometric Society.

Qu L.,Biostat Solutions Inc. | Nettleton D.,Iowa State University | Dekkers J.C.M.,Iowa State University
Biometrics | Year: 2012

Given a large number of t-statistics, we consider the problem of approximating the distribution of noncentrality parameters (NCPs) by a continuous density. This problem is closely related to the control of false discovery rates (FDR) in massive hypothesis testing applications, e.g., microarray gene expression analysis. Our methodology is similar to, but improves upon, the existing approach by Ruppert, Nettleton, and Hwang (2007, Biometrics, 63, 483-495). We provide parametric, nonparametric, and semiparametric estimators for the distribution of NCPs, as well as estimates of the FDR and local FDR. In the parametric situation, we assume that the NCPs follow a distribution that leads to an analytically available marginal distribution for the test statistics. In the nonparametric situation, we use convex combinations of basis density functions to estimate the density of the NCPs. A sequential quadratic programming procedure is developed to maximize the penalized likelihood. The smoothing parameter is selected with the approximate network information criterion. A semiparametric estimator is also developed to combine both parametric and nonparametric fits. Simulations show that, under a variety of situations, our density estimates are closer to the underlying truth and our FDR estimates are improved compared with alternative methods. Data-based simulations and the analyses of two microarray datasets are used to evaluate the performance in realistic situations. © 2012, The International Biometric Society.

Marshall S.,Biostat Solutions Inc. | Gennings C.,Virginia Commonwealth University | Teuschler L.K.,National Center for Environmental Assessment | Stork L.G.,Monsanto Corporation | And 3 more authors.
Risk Analysis | Year: 2013

When assessing risks posed by environmental chemical mixtures, whole mixture approaches are preferred to component approaches. When toxicological data on whole mixtures as they occur in the environment are not available, Environmental Protection Agency guidance states that toxicity data from a mixture considered "sufficiently similar" to the environmental mixture can serve as a surrogate. We propose a novel method to examine whether mixtures are sufficiently similar, when exposure data and mixture toxicity study data from at least one representative mixture are available. We define sufficient similarity using equivalence testing methodology comparing the distance between benchmark dose estimates for mixtures in both data-rich and data-poor cases. We construct a "similar mixtures risk indicator"(SMRI) (analogous to the hazard index) on sufficiently similar mixtures linking exposure data with mixtures toxicology data. The methods are illustrated using pyrethroid mixtures occurrence data collected in child care centers (CCC) and dose-response data examining acute neurobehavioral effects of pyrethroid mixtures in rats. Our method shows that the mixtures from 90% of the CCCs were sufficiently similar to the dose-response study mixture. Using exposure estimates for a hypothetical child, the 95th percentile of the (weighted) SMRI for these sufficiently similar mixtures was 0.20 (i.e., where SMRI <1, less concern; >1, more concern). © 2013 Society for Risk Analysis.

Suyundikov A.,Utah State University | Suyundikov A.,Biostat Solutions Inc. | Stevens J.R.,Utah State University | Corcoran C.,Utah State University | And 3 more authors.
BMC Genomics | Year: 2015

Background: Most currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study. Results: In simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered. Conclusions: When any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method. © 2015 Suyundikov et al.

Houston J.P.,Eli Lilly and Company | Houston J.P.,Indiana University | Houston J.P.,INC Research | Kohler J.,Biostat Solutions Inc. | And 7 more authors.
Journal of Clinical Psychiatry | Year: 2012

Objective: Pharmacogenomic analyses of weight gain during treatment with second-generation antipsychotics have resulted in a number of associations with variants in ankyrin repeat and kinase domain containing 1 (ANKK1)/dopamine D2 receptor (DRD2) and serotonin 2C receptor (HTR2C) genes. These studies primarily assessed subjects with schizophrenia who had prior antipsychotic exposure that may have influenced the amount of weight gained from subsequent therapies. We assessed the relationships between single-nucleotide polymorphisms (SNPs) in these genes with weight gain during treatment with olanzapine in a predominantly antipsychotic-naive population. Method: The association between 5 ANKK1, 54 DRD2, and 11 HTR2C SNPs and weight change during 8 weeks of olanzapine treatment was assessed in 4 pooled studies of 205 white patients with diagnoses other than schizophrenia who were generally likely to have had limited previous antipsychotic exposure. Results: The A allele of DRD2 rs2440390(A/G) was associated with greater weight gain in the entire study sample (P = .0473). Three HTR2C SNPs in strong linkage disequilibrium, rs6318, rs2497538, and rs1414334, were associated with greater weight gain in women but not in men (P = .0032, .0012, and .0031, respectively). A significant association with weight gain for 2 HTR2C SNPs previously reported associated with weight gain, -759C/T (rs3813929) and -697G/C (rs518147), was not found. Conclusions: Associations between weight gain and HTR2C and DRD2 variants in whites newly exposed to olanzapine may present opportunities for the individualization of medication selection and development based on differences in adverse events observed across genotype groups. Trial Registration: ClinicalTrials.gov identifiers: Study A: NCT00088036, Study B: NCT00091650, Study C: NCT00094549, Study D: NCT00035321. © Copyright 2012 Physicians Postgraduate Press, Inc.

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