Compound adaptive GPU design for clinical trials.

In clinical trials, several competing treatments are often carried out in the same trial period. The goal is to assess the performances of these different treatments according to some optimality criterion and minimize risks to the patients in the entire process of the study. For this, each coming patient is allocated sequentially to one of the treatments according to a mechanism defined by the optimality criterion. In practice, sometimes different optimality criteria, or the same criterion with different regimes, need to be considered to assess the treatments in the same study, so that each mechanism is also evaluated through the trail study. In this case, the question is how to allocate the treatments to the incoming patients so that the criteria/mechanisms of interest are assessed during the trail process, and the overall performance of the trial is optimized under the combined criteria or regimes. In this paper, we consider this problem by investigating a compound adaptive generalized Pólya urn design. Basic asymptotic properties of this design are also studied.

Global pattern of pairwise relationship in genetic network.

In recent times genetic network analysis has been found to be useful in the study of gene-gene interactions, and the study of gene-gene correlations is a special analysis of the network. There are many methods for this goal. Most of the existing methods model the relationship between each gene and the set of genes under study. These methods work well in applications, but there are often issues such as non-uniqueness of solution and/or computational difficulties, and interpretation of results. Here we study this problem from a different point of view: given a measure of pair wise gene-gene relationship, we use the technique of pattern image restoration to infer the optimal network pair wise relationships. In this method, the solution always exists and is unique, and the results are easy to interpret in the global sense and are computationally simple. The regulatory relationships among the genes are inferred according to the principle that neighboring genes tend to share some common features. The network is updated iteratively until convergence, each iteration monotonously reduces entropy and variance of the network, so the limit network represents the clearest picture of the regulatory relationships among the genes provided by the data and recoverable by the model. The method is illustrated with a simulated data and applied to real data sets.

Association of CD14 variant with prostate cancer in African American men.

BACKGROUND: African American men have the highest rates of prostate cancer worldwide, and immunogenetic studies suggest that people of African descent have increased susceptibility to diseases of inflammation. Since genetic susceptibility is an etiological factor in prostate cancer, we hypothesize that sequence variants in the promoter region of the CD14 gene that regulate inflammation may modify individual susceptibility to this disease. METHODS: The CD14 promoter was screened for single-nucleotide polymorphisms (SNPs) using dHPLC. One variant, -260 C>T (rs2569190), was genotyped via restriction digest in all study participants (264 cases and 188 controls). The association of disease status and the polymorphism was analyzed by unconditional logistic regression. Odds ratios with 95% confidence intervals were calculated, stratifying by ethnicity and adjusting for age. Two-sided P-values of < or =0.05 were considered as statistically significant. RESULTS: Eleven variants (four novel) were identified in the promoter region of CD14. A marginal association between the C genotypes (C/C + C/T) and prostate cancer was found (P = 0.07). When stratified by age, among men > or =55 years of age, the C genotypes were significantly associated with prostate cancer (P < 0.05). When stratified by self-reported ethnicity, African American males who had the C genotypes were at a higher risk for prostate cancer (P < 0.05). CONCLUSIONS: This is the first study to show an association between the C genotypes of the CD14 (-260) variant and prostate cancer which supports the hypothesis that genetic variation in the inflammatory process can contribute to prostate cancer susceptibility in African American men.

The Howard University Hospital experience with routineized HIV screening: a progress report.

BACKGROUND: Howard University Hospital (HUH) is the first hospital in the nation to have instituted a hospital-wide routine rapid HIV screening campaign as recommended by the CDC for healthcare settings. METHODS: HUH developed a protocol and implemented a hospital-wide routine HIV screening in October 2006. Rapid oral fluid-based HIV testing was conducted throughout the hospital using the OraSure OraQuick Advance Rapid HIV-1/2 Antibody Test. Patients with a preliminarily reactive test result were either referred for confirmatory testing or offered a Western Blot confirmatory test on-site and referred for follow-up care. This is a report on the progress of this program for the first eight months. RESULTS: Of the 9,817 patients offered HIV testing, 5,642 consented. The mean age of the screened population was 40.7 years. Ninety percent of the patients screened were black and 55% were female. A preliminarily reactive test result was identified in 139 patients for a seroprevalence rate of 2.46%. Of these patients, 136, or 98% were black; 63% were male and 37% were female. HIV prevalence in the overall sample, among blacks, and among both black males and females peaked in the 40-54 year old age group. Challenges were experienced initially in securing confirmatory tests. CONCLUSIONS: Hospital-wide routine HIV screening is both possible and productive. The routine HIV screening campaign instituted at Howard University Hospital has identified a significant number of previously unidentified HIV positive persons. Success in assuring confirmatory testing and transition to care improved as time progressed.

Molecular constitution of breast but not other reproductive tissues is rich in growth promoting molecules: a possible link to highest incidence of tumor growths.

In the current study we tested if highest incidence of benign as well as cancer growths in breast tissue is due to constitutive molecular composition of this tissue. To delineate the molecular basis, we compared the expression of nine functional gene modules (total 578 genes) that regulate major positive growth and negative inhibitory signals in normal breast with two other reproductive tissues, ovary and uterus. We present data to demonstrate that breast tissues constitutively have very highly elevated levels of several growth promoting molecules and diminished levels of inhibitory molecules which may, in part, contribute for highest incidence of tumor growths in this tissue.

Human leukocyte antigen (HLA)-B, DRB1, and DQB1 allotypes associated with disease and protection of trachoma endemic villagers.

PURPOSE: Trachoma remains the leading preventable infectious cause of blindness in developing countries. Human leukocyte antigen (HLA) associations with ocular disease severity and persistent Chlamydia trachomatis infection of Tanzanians living in trachoma-endemic villages were examined to determine possible protective candidate allotypes for vaccine development. METHODS: Buccal swab scrapes were taken from subjects in the Trichiasis Study Group (TSG), which studied females only, and the Family Trachoma Study (FTS), which compared persistently infected probands who had severe disease with disease-free siblings and parents. DNA was purified for polymerase chain reaction sequence-specific oligonucleotide identification of HLA-DRB1, DQB1, and B allotypes. Infection was detected from conjunctival scrapes using a C. trachomatis-specific PCR-enzyme immunoassay for the MOMP-1 gene. RESULTS: In the TSG, DR*B11 (odds ratio [OR], 0.48; 95% confidence interval [CI], 0.26-0.90; P=0.02) was significantly associated with lack of trichiasis, whereas HLA-B*07 (OR, 3.26; 95% CI, 1.42-7.49; P=0.004) and HLA-B*08 (OR, 5.12; 95% CI, 1.74-15.05; P=0.001) were associated with trichiasis. In addition, HLA-B*14 was significantly associated with inflammatory trachoma + follicular trachoma (OR, 3.76; 95% CI, 1.70-8.33; P=0.04). There were no significant allele frequencies for the FTS. CONCLUSIONS: The data suggest that HLA-DRB*11 may offer protection from trichiasis in trachoma hyperendemic villages. Complete allotype identification and designation of its respective protective CD4(+) T-cell antigens could provide a testable candidate vaccine for blindness prevention. Additionally, buccal swab DNA was sufficiently stable when acquired under harsh field conditions and stored long term in the freezer for low-resolution HLA typing.

Molecular risk assessment for breast cancer development in patients with ductal hyperplasias.

PURPOSE: It has been reported that approximately a million women are diagnosed with benign breast lesions that include ductal hyperplasias per year in the United States. Recent studies that followed women with benign lesions have established that about 8% to 9% of them will subsequently develop invasive breast cancer (IBC). However, currently, there are no means of identifying a subclass of "true precancerous tissues" in women with ductal hyperplasias who will subsequently develop cancer. The purpose of this study is to investigate whether expression of hyaluronoglucosaminidase 1 (HYAL1), a known tumor promoter, in hyperplastic tissues identifies a "true precancerous stage" and predicts subsequent IBC development. EXPERIMENTAL DESIGN: A retrospective study was conducted with archival benign tissues of various histologic types and clinical information on development/nondevelopment of IBC. The control group was hyperplastic tissues from women who had no prior history of IBC and did not develop cancer in 5 to 7 years after diagnosis (n = 81). The test group was hyperplastic tissues from patients who developed cancer (n = 82). HYAL1 expression was studied by immunohistochemistry, and the results were statistically analyzed for significant association to develop cancer (P value), specificity, sensitivity, positive predictive value, and negative predictive value. RESULTS: Statistical analysis of HYAL1 expression data showed very highly significant association between its expression and subsequent cancer development (P = 0) and very high sensitivity (0.83), specificity (0.84), positive predictive value (0.84), and negative predictive value (0.83). CONCLUSIONS: The expression of HYAL1 in ductal hyperplastic tissues is a strong predictor of subsequent development of IBC; therefore, it can be applied as a diagnostic marker either singly or in combination with other marker(s) to screen benign tissues to predict subsequent development of IBC. Detection at the precancerous stage and treatment could drastically cut down breast cancer incidence and deaths from it.

Gene copy number analysis for family data using semiparametric copula model.

Gene copy number changes are common characteristics of many genetic disorders. A new technology, array comparative genomic hybridization (a-CGH), is widely used today to screen for gains and losses in cancers and other genetic diseases with high resolution at the genome level or for specific chromosomal region. Statistical methods for analyzing such a-CGH data have been developed. However, most of the existing methods are for unrelated individual data and the results from them provide explanation for horizontal variations in copy number changes. It is potentially meaningful to develop a statistical method that will allow for the analysis of family data to investigate the vertical kinship effects as well. Here we consider a semiparametric model based on clustering method in which the marginal distributions are estimated nonparametrically, and the familial dependence structure is modeled by copula. The model is illustrated and evaluated using simulated data. Our results show that the proposed method is more robust than the commonly used multivariate normal model. Finally, we demonstrated the utility of our method using a real dataset.

An extension of the weighted dissimilarity test to association study in families.

Association studies for complex diseases based on pedigree haplotype or genotype data have received increasing attention in the last few years. The similarity tests are appealing for these studies because they take into account of the DNA structure, but they have blind areas on which significant association can not be detected. Recently, we developed a dissimilarity method for this problem based on independent haplotype data, which eliminates the blind areas of the existing methods. As DNA collected on families are common in practice, and the data are either of the form of genotype or haplotype. Here we extend our method for association study to data on families. It can be used to evaluate different designs in terms of power. Simulation studies confirmed that the extended method improves the type I error rate and power. Applying this method to the Genetic Analysis Workshop 14 alcoholism data, we find that markers rs716581, rs1017418, rs1332184 and rs1943418 on chromosomes 1, 2, 9 and 18 yield strong signal (with P value 0.001 or lower) for association with alcoholism. Our work can serve as a guide in the design of association studies in families.

Expression of carcinoembryonic antigen cell adhesion molecule 6 oncoprotein in atypical ductal hyperplastic tissues is associated with the development of invasive breast cancer.

BACKGROUND: Epidemiologic studies have established that women with prior atypical ductal hyperplastic (ADH) lesions have a 5-fold increased risk of developing invasive breast cancer (IBC). However, there is currently no means of identifying a subclass of ADH from women who will most likely develop cancer. The purpose of this study is to investigate whether elevated expression of carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) in ADH tissues is associated with the development of IBC. METHODS: A retrospective study was conducted with archival ADH tissues and clinical information on the development/nondevelopment of IBC. The control group was ADH from patients who had no prior history of IBC and did not develop cancer within 5 years after the diagnosis of ADH (n = 44). The test group was ADH from patients who either developed cancer concurrently or subsequently after diagnosis (ADHC; n = 44). The expression of CEACAM6 was studied by immunohistochemistry and the results were statistically analyzed for significant association to develop cancer (P value), specificity, sensitivity, positive predictive value, and negative predictive value. RESULTS: Of the 44 control ADH tissues from patients with no history of cancer, 9 were positive for CEACAM6. Among the ADHC tissues, 40 of 44 samples were positive. Statistical analysis of CEACAM6 expression data showed a significant association between its expression and cancer development, high sensitivity, specificity, positive predictive value, and negative predictive value. CONCLUSIONS: The expression of CEACAM6 in ADH lesions is strongly associated with the development of IBC, therefore, it can be applied as a diagnostic marker either singly or in combination with other marker(s) to predict IBC development in women with ADH lesions. It could also be a potential molecular therapeutic target for preventing IBC.

Detecting disease gene in DNA haplotype sequences by nonparametric dissimilarity test.

Association studies for complex diseases based on haplotype data have received increasing attention in the last few years. A commonly used nonparametric method, which takes haplotype structure into consideration, is to use the U-statistic to compare the similarities between genetic compositions in the case and control populations. Although the method and its variants are convenient to use in practice, there are some areas where the tests cannot detect even large differences between cases and controls. To overcome this problem and enhance the power, we propose a new form of the weighted U-statistic, which directly compares the dissimilarity between the haplotype structures in the case and control populations. We show that this test statistic is asymptotically a linear combination of the absolute values of normal random variables under the null hypothesis, and shifts strictly toward the right under the alternative, and therefore has no blind areas of detection. Simulation studies indicate that our test statistic overcomes the weakness of the existing ones and is robust and powerful as well.

Variance components model with disequilibria.

The variance components (VC) model has been popular for genetic analysis. It has received wide applications in a variety of genetic practices, and been extended to various forms for different settings. However, most of the existing VC models are, explicitly or implicitly, under the assumption of the Hardy-Weinberg and/or linkage equilibria, which is impractical in some realistic settings since more or less deviations from this assumption are common. We propose a new VC model that incorporates both these disequilibria, and includes the existing models as special cases. The corresponding variance components are computed for some commonly used relative pairs conditional on the observed marker identity-by-descent data. Parameters can be estimated by the traditional methods such as the maximum likelihood estimate. Simulation studies suggest that this extended model improves inference significantly over the existing models when deviations of these disequilibria are present.

A statistical framework for haplotype block inference.

The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.

Genes, age, and alcoholism: analysis of GAW14 data.

A genetic analysis of age of onset of alcoholism was performed on the Collaborative Study on the Genetics of Alcoholism data released for Genetic Analysis Workshop 14. Our study illustrates an application of the log-normal age of onset model in our software Genetic Epidemiology Models (GEMs). The phenotype ALDX1 of alcoholism was studied. The analysis strategy was to first find the markers of the Affymetrix SNP dataset with significant association with age of onset, and then to perform linkage analysis on them. ALDX1 revealed strong evidence of linkage for marker tsc0041591 on chromosome 2 and suggestive linkage for marker tsc0894042 on chromosome 3. The largest separation in mean ages of onset of ALDX1 was 19.76 and 24.41 between male smokers who are carriers of the risk allele of tsc0041591 and the non-carriers, respectively. Hence, male smokers who are carriers of marker tsc0041591 on chromosome 2 have an average onset of ALDX1 almost 5 years earlier than non-carriers.

A study of genetic association with electrophysiological measures related to alcoholism: GAW14 data.

Recently, alcohol-related traits have been shown to have a genetic component. Here, we study the association of specific genetic measures in one of the three sets of electrophysiological measures in families with alcoholism distributed as part of the Genetic Analysis Workshop 14 data, the NTTH (non-target case of Visual Oddball experiment for 4 electrode placements) phenotypes: ntth1, ntth2, ntth3, and ntth4. We focused on the analysis of the 786 Affymetrix markers on chromosome 4. Our desire was to find at least a partial answer to the question of whether ntth1, ntth2, ntth3, and ntth4 are separately or jointly genetically controlled, so we studied the principal components that explain most of the covariation of the four quantitative traits. The first principal component, which explains 70% of the covariation, showed association but not genetic linkage to two markers: tsc0272102 and tsc0560854. On the other hand, ntth1 appeared to be the trait driving the variation in the second principal component, which showed association and genetic linkage at markers in four regions: tsc0045058, tsc1213381, tsc0055068, and tsc0051777 at map distances 53.26, 85.42, 89.31, and 172.86, respectively. These results show that the partial answer to our starting question for this brief analysis is that the NTTH phenotypes are not jointly genetically controlled. The component ntth1 displays marked genetic linkage.

Which strategy is better for linkage analysis: single-nucleotide polymorphisms or microsatellites? Evaluation by identity-by-state-identity-by-descent transformation affected sib-pair method on GAW14 data.

The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state - identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods.

Identifying the susceptibility gene(s) in a set of trait-linked genes using genotype data.

There are generally three steps to isolate a disease linkage-susceptibility gene: genome-wide scan, fine mapping, and, last, positional cloning. The last step is time consuming and involves intensive laboratory work. In some cases, fine mapping cannot proceed further on a set of markers because they are tightly linked. For years, genetic statisticians have been trying different ways to narrow the fine-mapping results to provide some guidance for the next step of laboratory work. Although these methods are practical and efficient, most of them are based on IBD data, which usually can be inferred only from the genotype data with some uncertainty. The corresponding methods thus have no greater power than one using genotype data directly. Also, IBD-based methods apply only to relative pair data. Here, using genotype data, we have developed a statistical hypothesis-testing method to pinpoint a SNP, or SNPs, suspected of responsibility for a disease trait linkage among a set of SNPs tightly linked in a region. Our method uses genotype data of affected individuals or case-control studies, which are widely available in the laboratory. The testing statistic can be constructed using any genotype-based disease-marker disequilibrium measure and is asymptotically distributed as a chi-square mixture. This method can be used for singleton data, relative pair data, or general pedigree data. We have applied the method to simulated data as well as a real data set; it gives satisfactory results.

Exact test of Hardy-Weinberg equilibrium by Markov chain Monte Carlo.

The assumption of Hardy-Weinberg equilibrium (HWE) among alleles is of fundamental importance in genetic studies. There are numerous testing methods for it using genotype counts data. The exact test is used when the sample size is not large enough for asymptotic approximations. There are several numerical methods to carry out this test, such as complete enumeration, Monte Carlo and Markov chain Monte Carlo simulations. Complete enumeration is impractical in many applications, especially when the table counts are large. The Monte Carlo method is simple to use but still difficult when the table counts become large. The Markov chain Monte Carlo method, by sampling a sub-table each time, is suitable for this latter situation. Based on switches among a few (no more than four) cells, the existing Markov chain samplers are highly dependent and inefficient for large tables. Here we consider a new Markov chain sampling, in which a sub-table of user-specified size is updated at each iteration. The resulting chain is less dependent, and the sampling is flexible and efficient. The conventional test for HWE is based on a few test statistics, such as the likelihood and the chi-squared statistic. To expand the family of test statistics, we consider a class of divergence measures for the departure of HWE. Examples are given as illustrations.

Interactions between genetic and reproductive factors in breast cancer risk in a population-based sample of African-American families.

Incidence of breast cancer (BC) varies among ethnic groups, with higher rates in white than in African-American women. Until now, most epidemiological and genetic studies have been carried out in white women. To investigate whether interactions between genetic and reproductive risk factors may explain part of the ethnic disparity in BC incidence, a genetic epidemiology study was conducted, between 1989 and 1994, at the Howard University Cancer Center (Washington, DC), which led to the recruitment of 245 African-American families. Segregation analysis of BC was performed by use of the class D regressive logistic model that allows for censored data to account for a variable age of onset of disease, as implemented in the REGRESS program. Segregation analysis of BC was consistent with a putative dominant gene effect (P < 0.000001) and residual sister-dependence (P < 0.0001). This putative gene was found to interact significantly with age at menarche (P = 0.048), and an interaction with a history of spontaneous abortions was suggested (P = 0.08). A late age at menarche increased BC risk in gene carriers but had a protective effect in non-gene carriers. A history of spontaneous abortions had a protective effect in gene carriers and increased BC risk in non-gene carriers. Our findings agree partially with a similar analysis of French families showing a significant gene x parity interaction and a suggestive gene x age at menarche interaction. Investigating gene x risk factor interactions in different populations may have important implications for further biological investigations and for BC risk assessment.

Two new recursive likelihood calculation methods for genetic analysis.

Recursive likelihood calculations for genetic analysis with ungenotyped pedigree data employ variations of the Elston-Stewart (ES) or the Lander-Green (LG) algorithms. With the ES algorithm, the number of loci may be limited but not the pedigree size. With the LG algorithm, the reverse is the case. We introduce two new algorithms for the computation of regressive likelihoods for pedigrees with multivariate traits. The first is an alternative formulation of our existing model, which leads to a simpler form in the binary trait, polygenic and mixed model cases. The second is an approximation model, which is computationally efficient. These methods apply to both continuous and binary traits, in the oligogenic and polygenic cases. Both methods coincide in the binary case. We considered these methods for cases in which all the traits are controlled by a single locus, with each trait controlled by one locus independent to the others. Simulation studies and analysis of a real data are presented for segregation analysis as illustrations. These methods can also be used in other model-based analyses. These methods are implemented in G.E.M.S., the genetic epidemiology models software.