Mixed modeling and sample size calculations for identifying housekeeping genes.

Normalization of gene expression data using internal control genes that have biologically stable expression levels is an important process for analyzing reverse transcription polymerase chain reaction data. We propose a three-way linear mixed-effects model to select optimal housekeeping genes. The mixed-effects model can accommodate multiple continuous and/or categorical variables with sample random effects, gene fixed effects, systematic effects, and gene by systematic effect interactions. We propose using the intraclass correlation coefficient among gene expression levels as the stability measure to select housekeeping genes that have low within-sample variation. Global hypothesis testing is proposed to ensure that selected housekeeping genes are free of systematic effects or gene by systematic effect interactions. A gene combination with the highest lower bound of 95% confidence interval for intraclass correlation coefficient and no significant systematic effects is selected for normalization. Sample size calculation based on the estimation accuracy of the stability measure is offered to help practitioners design experiments to identify housekeeping genes. We compare our methods with geNorm and NormFinder by using three case studies. A free software package written in SAS (Cary, NC, U.S.A.) is available at http://d.web.umkc.edu/daih under software tab.

Bioterrorism and disaster preparedness among medical specialties.

OBJECTIVE: A core priority of all medical specialties includes information for members regarding inherent priorities and principles. The authors sought to investigate the priority and contribution of various medical specialties to the fields of bioterrorism, terrorism, disaster preparedness, and emergency preparedness. DESIGN: A mixed study design (quantitative and qualitative) was used to identify pertinent characteristics of various medical specialties. A scored survey analysis of resources available from the representative organizations and/or societies of the primary medical specialties and select subspecialties was examined and scored based on availability, ease of accessibility, updated status, and content. A MEDLINE search completed through PubMed using the medical subject headings bioterrorism, terrorism, disaster preparedness, and emergency preparedness coupled with specific medical specialties was conducted to assess the involvement and contribution of each to the medical literature. MAIN OUTCOME MEASURES: The primary study outcome was to evaluate the priority of and existing resources available to members for bioterrorism/terrorism and disaster/emergency preparedness among various medical specialties as reflected by their representative organizations and scientific publication. RESULTS: The search of individual medical specialties and of the medical literature (2000-2010) revealed that these topics (via keywords bioterrorism, terrorism, disaster preparedness, and emergency preparedness) are indeed a priority topic for the majority of medical specialties. A number of specialties with expectant priority in these topics were confirmed. All seven primary care specialties demonstrated a core priority of these topics and offered resources. The MEDLINE (PubMed) search yielded 7,228 articles published from 2000 to 2010. CONCLUSION: Bioterrorism/terrorism and disaster/ emergency preparedness are priority topics of most medical specialties. This core priority is demonstrated by both the medical specialty resources in addition to the contribution of scientific articles from these medical specialties. This reflects the diverse medical care that is necessary for terrorist threats and the collaborative efforts that will help to make the medical response to these threats more cohesive.

Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes.

BACKGROUND: Multifactor Dimensionality Reduction (MDR) is a popular and successful data mining method developed to characterize and detect nonlinear complex gene-gene interactions (epistasis) that are associated with disease susceptibility. Because MDR uses a combinatorial search strategy to detect interaction, several filtration techniques have been developed to remove genes (SNPs) that have no interactive effects prior to analysis. However, the cutoff values implemented for these filtration methods are arbitrary, therefore different choices of cutoff values will lead to different selections of genes (SNPs). METHODS: We suggest incorporating a global test of p-values to filtration procedures to identify the optimal number of genes/SNPs for further MDR analysis and demonstrate this approach using a ReliefF filter technique. We compare the performance of different global testing procedures in this context, including the Kolmogorov-Smirnov test, the inverse chi-square test, the inverse normal test, the logit test, the Wilcoxon test and Tippett's test. Additionally we demonstrate the approach on a real data application with a candidate gene study of drug response in Juvenile Idiopathic Arthritis. RESULTS: Extensive simulation of correlated p-values show that the inverse chi-square test is the most appropriate approach to be incorporated with the screening approach to determine the optimal number of SNPs for the final MDR analysis. The Kolmogorov-Smirnov test has high inflation of Type I errors when p-values are highly correlated or when p-values peak near the center of histogram. Tippett's test has very low power when the effect size of GxG interactions is small. CONCLUSIONS: The proposed global tests can serve as a screening approach prior to individual tests to prevent false discovery. Strong power in small sample sizes and well controlled Type I error in absence of GxG interactions make global tests highly recommended in epistasis studies.