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.^

Use of tabletop exercises for disaster preparedness training

Background. In public health preparedness, disaster preparedness refers to the strategic planning of responses to all types of disasters. Preparation and training for disaster response can be conducted using different teaching modalities, ranging from discussion-based programs such as seminars, drills and tabletop exercises to more complex operation-based programs such as functional exercises and full-scale exercises. Each method of instruction has its advantages and disadvantages. Tabletop exercises are facilitated discussions designed to evaluate programs, policies, and procedures; they are usually conducted in a classroom, often with tabletop props (e.g. models, maps or diagrams). ^ Objective. The overall goal of this project was to determine whether tabletop exercises are effective teaching modalities for disaster preparedness, with an emphasis on intentional chemical exposure. ^ Method. The target audience for the exercise was the Medical Reserve Brigade of the Texas State Guard, a group of volunteer healthcare providers and first responders who prepare for response to local disasters. A new tabletop exercise was designed to provide information on the complex, interrelated organizations within the national disaster preparedness program that this group would interact with in the event of a local disaster. This educational intervention consisted of a four hour multipart program that included a pretest of knowledge, lecture series, an interactive group discussion using a mock disaster scenario, a posttest of knowledge, and a course evaluation. ^ Results. Approximately 40 volunteers attended the intervention session; roughly half (n=21) had previously participated in a full scale drill. There was an 11% improvement in fund of knowledge between the pre- and post-test scores (p=0.002). Overall, the tabletop exercise was well received by those with and without prior training, with no significant differences found between these two groups in terms of relevance and appropriateness of content. However, the separate components of the tabletop exercise were variably effective, as gauged by written text comments on the questionnaire. ^ Conclusions. Tabletop exercises can be a useful training modality in disaster preparedness, as evidenced by improvement in knowledge and qualitative feedback on its value. Future offerings could incorporate recordings of participant responses during the drill, so that better feedback can be provided to them. Additional research should be conducted, using the same or similar design, in different populations that are stakeholders in disaster preparedness, so that the generalizability of these findings can be determined.^