The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^

Cross-culture perspectives on fear of HIV disease and AIDS in health care workers

Health care workers have been known to carry into the workplace a variety of judgmental and negative attitudes towards their patients. In no other area of patient care has this issue been more pronounced as in the management of patients with AIDS. Health care workers have refused to treat or manage patients with AIDS and have often treated them more harshly than identically described leukemia patients. Some health care institutions have simply refused to admit patients with AIDS and even recent applicants to medical colleges and schools of nursing have indicated a preference for schools in areas with low prevalence of HIV disease. Since the attitudes of health care workers do have significant consequences on patient management, this study was carried out to determine the differences in clinical practice in Nigeria and the United States of America as it relates to knowledge of a patient's HIV status, determine HIV prevalence and culture in each of the study sites and how they impact on infection control practices, determine the relationship between infection control practices and fear of AIDS, and also determine the predictors of safe infection control practices in each of the study sites.^ The study utilized the 38-item fear of AIDS scale and the measure of infection control questionnaire for its data. Questionnaires were administered to health care workers at the university teaching hospital sites of Houston, Texas and Calabar in Nigeria. Data was analyzed using a chi-square test, and where appropriate, a student t-tests to establish the demographic variables for each country. Factor analysis was done using principal components analysis followed by varimax rotation to simple structure. The subscale scores for each study site were compared using t-tests (separate variance estimates) and utilizing Bonferroni adjustments for number of tests. Finally, correlations were carried out between infection control procedures and fear of AIDS in each study site using Pearson-product moment correlation coefficients.^ The study revealed that there were five dimensions of the fear of AIDS in health care workers, namely fear of loss of control, fear of sex, fear of HIV infection through blood and illness, fear of death and medical interventions and fear of contact with out-groups. Fear of loss of control was the primary area of concern in the Nigerian health care workers whereas fear of HIV infection through blood and illness was the most important area of AIDS related feats in United States health care workers. The study also revealed that infection control precautions and practices in Nigeria were based more on normative and social pressures whereas it was based on knowledge of disease transmission, supervision and employee discipline in the United States, and thus stresses the need for focused educational programs in health care settings that emphasize universal precautions at all times and that are sensitive to the cultural nuances of that particular environment. ^

Childhood cancer survivor satisfaction with characteristics of clinical care

This study investigated the characteristics of a clinic that affect how satisfied survivors of childhood cancer are with their medical care. Questionnaire and interview data from the Passport for Care: Texas Implementation project collected between January 2011 to April 2012 were analyzed. Eleven clinics in Texas participated. Questionnaire respondents were childhood cancer survivor patients who had been off therapy for at least 2 years, or their parents. Interview respondents were clinical providers or research staff at the participating clinics. The outcomes evaluated were answers to a single question on satisfaction with care and a composite Percent Satisfaction Score created from seven other questionnaire items that were correlated (Spearman Rho >0.3) with the question on satisfaction. The following characteristics were also evaluated: sex, age, race, education, and type of cancer. The following clinic indicators were evaluated: type of clinic (general vs. dedicated cancer survivor clinics), number of providers, number of survivors, ratio of survivors/providers, distribution of handouts, distribution of treatment summaries, and use of Children's Oncology Group (COG) guidelines. ^ The only demographic characteristic that affected satisfaction was race. A Kruskal-Wallis test showed a statistically significant difference (Chi-square 6.129, 2 d.f., p = 0.0467). To analyze this further, Wilcoxon Rank Sum test of pairings of the three groups were performed. A Bonferroni correction for multiple testing was applied, with p = 0.017 indicating significance at alpha = 0.05. There was no significant difference between the White and Hispanic groups or between the Hispanic and "Other" groups. For the White and "Other" groups there was a significant difference for the satisfaction item (p = 0.0123) but not for the Percent Satisfaction Score (p = 0.0289). These results suggest that race may influence satisfaction and should be evaluated further in future studies. ^ None of the clinic indicators affected the Percent Satisfaction Score. Going to a clinic that distributed patient information handouts (Wilcoxon Rank Sum p = 0.048) and going to a clinic with >=100 survivors (Wilcoxon Rank Sum p = 0.021) were associated with increased satisfaction. The population of childhood cancer survivors is a growing group of individuals with special health needs. In the future survivors will likely seek medical care in a variety of clinical settings, so it is important to investigate features to improve patient satisfaction with clinical care.^