Identification of glyoxalase 1 polymorphisms associated with enzyme activity

The glyoxalase system and its main enzyme, glyoxalase 1 (GLO1), protect cells from advanced glycation end products (AGEs), such as methylglyoxal (MG) and other reactive dicarbonyls, the formation of which is increased in diabetes patients as a result of excessive glycolysis. MG is partly responsible for harmful protein alterations in living cells, notably in neurons, leading to their dysfunction, and recent studies have shown a negative correlation between GLO1 expression and tissue damage. Neuronal dysfunction is a common diabetes complication due to elevated blood sugar levels, leading to high levels of AGEs. The aim of our study was to determine whether single nucleotide polymorphisms (SNPs) in the GLO1 gene influence activity of the enzyme. In total, 125 healthy controls, 101 type 1 diabetes, and 100 type 2 diabetes patients were genotyped for three common SNPs, rs2736654 (A111E), rs1130534 (G124G), and rs1049346 (5′-UTR), in GLO1. GLO1 activity was determined in whole blood lysates for all participants of the study.     

Unconditional Exact Tests for Equivalence or Noninferiority for Paired Binary Endpoints

Problems of establishing equivalence or noninferiority between two medical diagnostic procedures involve comparisons of the response rates between correlated proportions. When the sample size is small, the asymptotic tests may not be reliable. This article proposes an unconditional exact test procedure to assess equivalence or noninferiority. Two statistics, a sample-based test statistic and a restricted maximum likelihood estimation (RMLE)-based test statistic, to define the rejection region of the exact test are considered. We show the p-value of the proposed unconditional exact tests can be attained at the boundary point of the null hypothesis. Assessment of equivalence is often based on a comparison of the confidence limits with the equivalence limits. We also derive the unconditional exact confidence intervals on the difference of the two proportion means for the two test statistics. A typical data set of comparing two diagnostic procedures is analyzed using the proposed unconditional exact and asymptotic methods. The p-value from the unconditional exact tests is generally larger than the p-value from the asymptotic tests. In other words, an exact confidence interval is generally wider than the confidence interval obtained from an asymptotic test.     

Identification of target proteins of clinical immunity to Plasmodium falciparum in a region of low malaria transmission

The target molecules of antibodies against falciparum malaria remain largely unknown. Recently we have identified multiple proteins as targets of immunity against Plasmodium falciparum using African serum samples. To investigate whether potential targets of clinical immunity differ with transmission intensity, we assessed immune responses in residents of low malaria transmission region in Thailand. Malaria asymptomatic volunteers (Asy: n = 19) and symptomatic patients (Sym: n = 21) were enrolled into the study. Serum immunoreactivity to 186 wheat germ cell-free system (WGCFS)-synthesized recombinant P. falciparum asexual-blood stage proteins were determined by AlphaScreen, and subsequently compared between the study groups. Forty proteins were determined as immunoreactive with antibody responses to 35 proteins being higher in Asy group than in Sym group. Among the 35 proteins, antibodies to MSP3, MSPDBL1, RH2b, and MSP7 were significantly higher in Asy than Sym (unadjusted p < 0.005) suggesting these antigens may have a protective role in clinical malaria. MSP3 reactivity remained significantly different between Asy and Sym groups even after multiple comparison adjustments (adjusted p = 0.033). Interestingly, while our two preceding studies using African sera were conducted differently (e.g., cross-sectional vs. longitudinal design, observed clinical manifestation vs. functional activity), those studies similarly identified MSP3 and MSPDBL1 as potential targets of protective immunity. This study further provides a strong rationale for the application of WGCFS-based immunoprofiling to malaria vaccine candidate and biomarker discovery even in low or reduced malaria transmission settings.     

Monte Carlo algorithms for Hardy-Weinberg proportions

The Hardy-Weinberg law is among the most important principles in the study of biological systems. Given its importance, many tests have been devised to determine whether a finite population follows Hardy-Weinberg proportions. Because asymptotic tests can fail, Guo and Thompson developed an exact test; unfortunately, the Monte Carlo method they proposed to evaluate their test has a running time that grows linearly in the size of the population N. Here, we propose a new algorithm whose expected running time is linear in the size of the table produced, and completely independent of N. In practice, this new algorithm can be considerably faster than the original method.     

Multiple-testing strategy for analyzing cDNA array data on gene expression

An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ability of the study to resolve effects. In our work, we are more interested in selecting most of the affected genes rather than protecting against a few false positives. An optimum cutoff, i.e., the best set given the data, depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. We select the cutoff by a decision-theoretic method analogous to methods developed for receiver operating characteristic curves. In addition, we estimate the false discovery rate and the false nondiscovery rate associated with any cutoff value. Two functional genomics studies that were designed to assess a treatment effect are used to illustrate how the methods allowed the investigators to determine a cutoff to suit their research goals.     

Inferences on the common mean of several normal populations based on the generalized variable method

This article presents procedures for hypothesis testing and interval estimation of the common mean of several normal populations. The methods are based on the concepts of generalized p-value and generalized confidence limit. The merits of the proposed methods are evaluated numerically and compared with those of the existing methods. Numerical studies show that the new procedures are accurate and perform better than the existing methods when the sample sizes are moderate and the number of populations is four or less. If the number of populations is five or more, then the generalized variable method performs much better than the existing methods regardless of the sample sizes. The generalized variable method and other existing methods are illustrated using two examples.     

Sample size calculation for multiple testing in microarray data analysis

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.     

On sample size and inference for two-stage adaptive designs

Proschan and Hunsberger (1995, Biometrics 51, 1315-1324) proposed a two-stage adaptive design that maintains the Type I error rate. For practical applications, a two-stage adaptive design is also required to achieve a desired statistical power while limiting the maximum overall sample size. In our proposal, a two-stage adaptive design is comprised of a main stage and an extension stage, where the main stage has sufficient power to reject the null under the anticipated effect size and the extension stage allows increasing the sample size in case the true effect size is smaller than anticipated. For statistical inference, methods for obtaining the overall adjusted p-value, point estimate and confidence intervals are developed. An exact two-stage test procedure is also outlined for robust inference.