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Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study of temporomandibular disorders (TMD), motivates this work. Numerous measures of interest are collected at enrollment, such as the number of comorbid pain conditions from which a participant suffers. Examining the potential genetic basis of these measures is of secondary interest. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. We propose a general method for the analysis of arbitrary phenotypes utilizing inverse probability weighting and bootstrapping for standard error estimation. The method may be applied to the complicated association tests used in next-generation sequencing studies, such as analyses of haplotypes with ambiguous phase. Simulation studies show that our method performs as well as competing methods when they are applicable and yield promising results for outcome types, such as time-to-event, to which other methods may not apply. The method is applied to the OPPERA baseline case–control genetic study.

Nonlinear dose–response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Modeling such responses is a crucial component of toxicity testing and chemical screening. Traditional model fitting methods such as nonlinear least squares (NLS) are very sensitive to initial parameter values and often had convergence failure. The use of evolutionary algorithms (EAs) has been proposed to address many of the limitations of traditional approaches, but previous methods have been limited in the types of models they can fit. Therefore, we propose the use of an EA for dose–response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose–response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. Compared with NLS, the new method provides stable and robust solutions without sensitivity to initial values.

Abstract. Background:. Chronic facial pain often overlaps with pain experienced elsewhere in the body, although previous studies have focused on a few, selected pain conditions when assessing the degree of overlap. Aim:. To quantify the degree of overlap between facial pain and pain reported at multiple locations throughout the body. Methods:. Data were from a case–control study of US adults participating in the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project. They were interviewed to determine the presence of chronic facial pain (n = 424 cases) or its absence (n = 912 controls). A mailed questionnaire with a body drawing asked about pain at other locations. Odds ratios (ORs) and 95% confidence limits (95% CLs) quantified the degree of overlap between facial pain and pain at other locations. For replication, cross-sectional data were analyzed from the UK Biobank study (n = 459,604 participants) and the US National Health Interview Survey (n = 27,731 participants). Results:. In univariate analysis, facial pain had greatest overlap with headache (OR = 14.2, 95% CL = 9.7–20.8) followed by neck pain (OR = 8.5, 95% CL = 6.5–11.0), whereas overlap decreased substantially (ORs of 4.4 or less) for pain at successively remote locations below the neck. The same anatomically based ranking of ORs persisted in multivariable analysis that adjusted for demographics and risk factors for facial pain. Findings were replicated in the UK Biobank study and the US National Health Interview Survey. The observed anatomical selectivity in the degree of overlap could be a consequence of neurosensory and/or affective processes that differentially amplify pain according to its location.

Dental caries is the most prevalent disease in humans globally. Efforts to control it have been invigorated by an increasing knowledge of the oral microbiome composition. This study aimed to evaluate the bacterial diversity in occlusal biofilms and its relationship with clinical surface diagnosis and dietary habits. Anamneses were recorded from thirteen 12-year-old children. Biofilm samples collected from occlusal surfaces of 46 permanent second molars were analyzed by 16S rRNA amplicon sequencing combined with the BLASTN-based search algorithm for species identification. The overall mean decayed, missing and filled surfaces modified index [DMFSm Index, including active white spot lesions (AWSL)] value was 8.77±7.47. Biofilm communities were highly polymicrobial collectively, representing 10 bacterial phyla, 25 classes, 29 orders, 58 families, 107 genera, 723 species. Streptococcus sp_Oral_Taxon_065, Corynebacterium matruchotii, Actinomyces viscosus, Actinomyces sp_Oral_Taxon_175, Actinomyces sp_Oral_Taxon_178, Actinomyces sp_Oral_Taxon_877, Prevotella nigrescens, Dialister micraerophilus, Eubacterium_XI G 1 infirmum were more abundant among surfaces with AWSL, and Streptococcus gordonii, Streptococcus sp._Oral_Taxon_058, Enterobacter sp._str._638 Streptococcus australis, Yersinia mollaretii, Enterobacter cloacae, Streptococcus sp._Oral_Taxon_71, Streptococcus sp._Oral_Taxon_F11, Centipeda sp._Oral_Taxon_D18 were more abundant among sound surfaces. Streptococcus mutans was detected on all surfaces in all patients, while Streptococcus sobrinus was detected only in three patients (mean relative abundances 7.1% and 0.6%, respectively). Neither species differentiated healthy from diseased sites. Diets of nine of the subjects were scored as high in fermentable carbohydrates (≧2X/day between meals). A direct association between relative abundances of bacteria and carbohydrate consumption was observed among 18 species. High consumption of fermentable carbohydrates and sound surfaces were associated with a reduction in bacterial diversity. PCoA plots displayed differences in bacterial community profiles between sound and diseased surfaces. Our study showed that, in addition to mutans streptococci, other species may be associated with the initiation of dental caries on occlusal surfaces, and that biofilm diversity of tooth surfaces is influenced by carbohydrate consumption and a surface's health status.

Nearest shrunken centroids (NSC) is a popular classification method for microarray data. NSC calculates centroids for each class and "shrinks" the centroids toward 0 using soft thresholding. Future observations are then assigned to the class with the minimum distance between the observation and the (shrunken) centroid. Under certain conditions the soft shrinkage used by NSC is equivalent to a LASSO penalty. However, this penalty can produce biased estimates when the true coefficients are large. In addition, NSC ignores the fact that multiple measures of the same gene are likely to be related to one another. We consider several alternative genewise shrinkage methods to address the aforementioned shortcomings of NSC. Three alternative penalties were considered: the smoothly clipped absolute deviation (SCAD), the adaptive LASSO (ADA), and the minimax concave penalty (MCP). We also showed that NSC can be performed in a genewise manner. Classification methods were derived for each alternative shrinkage method or alternative genewise penalty, and the performance of each new classification method was compared with that of conventional NSC on several simulated and real microarray data sets. Moreover, we applied the geometric mean approach for the alternative penalty functions. In general the alternative (genewise) penalties required fewer genes than NSC. The geometric mean of the class-specific prediction accuracies was improved, as well as the overall predictive accuracy in some cases. These results indicate that these alternative penalties should be considered when using NSC.

PURPOSE/OBJECTIVES: To determine if intensity modulated radiation therapy (IMRT) in the post-operative setting for gastric cancer was associated with reduced toxicity compared to 3D conformal radiation therapy (3DCRT). MATERIALS/METHODS: This retrospective study includes 24 patients with stage IB-IIIB gastric cancer consecutively treated from 2001-2010. All underwent surgery followed by adjuvant chemoradiation. Concurrent chemotherapy consisted of 5-FU/leucovorin (n = 21), epirubicin/cisplatin/5FU (n = 1), or none (n = 2). IMRT was utilized in 12 patients and 3DCRT in 12 patients. For both groups, the target volume included the tumor bed, anastomosis, gastric stump, and regional lymphatics. RESULTS: Median follow-up for the entire cohort was 19 months (range 0.4-8.5 years), and 49 months (0.5-8.5 years) in surviving patients. The 3DCRT group received a median dose of 45 Gy, and the IMRT group received a median dose of 50.4 Gy (p = 0.0004). For the entire cohort, 3-year overall survival (OS) was 40% and 3-year disease free survival (DFS) was 41%. OS and DFS did not differ significantly between the groups. Acute toxicity was similar. Between 3DCRT and IMRT groups, during radiotherapy, median weight lost (3.2 vs. 3.3 kg, respectively; p = 0.47) and median percent weight loss were similar (5.0% vs. 4.3%, respectively; p = 0.43). Acute grade 2 toxicity was experienced by 8 patients receiving 3DCRT and 11 receiving IMRT (p = 0.32); acute grade 3 toxicity occurred in 1 patient receiving 3DCRT and none receiving IMRT (p = 1.0). No patients in either cohort experienced late grade 3 toxicity, including renal or gastrointestinal toxicity. At last follow up, the median increase in creatinine was 0.1 mg/dL in the IMRT group and 0.1 mg/dL in the 3DCRT group (p = 0.78). CONCLUSION: This study demonstrates that adjuvant chemoradiation for gastric cancer with IMRT to 50.4 Gy was well-tolerated and compared similarly in toxicity with 3DCRT to 45 Gy.

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.

An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer.