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.^

A critical review: Why isn't "cold pasteurization" being used widespread as a method to prevent food-related illnesses as a result of the consumption of ready-to-eat, fresh fruits, and vegetables

The Centers for Disease Control estimates that foodborne diseases cause approximately 76 million illnesses, 325,000 hospitalizations, and 5,000 deaths in the United States each year. The American public is becoming more health conscious and there has been an increase in the dietary intake of fresh fruits and vegetables. Affluence and demand for convenience has allowed consumers to opt for pre-processed packaged fresh fruits and vegetables. These pre-processed foods are considered Ready-to-Eat. They have many of the advantages of fresh produce without the inconvenience of processing at home. After seeing a decline in food-related illnesses between 1996 and 2004, due to an improvement in meat and poultry safety, tainted produce has tilted the numbers back. This has resulted in none of the Healthy People 2010 targets for food-related illness reduction being reached. Irradiation has been shown to be effective in eliminating many of the foodborne pathogens. The application of irradiation as a food safety treatment has been widely endorsed by many of the major associations involved with food safety and public health. Despite these endorsements there has been very little use of this technology to date for reducing the disease burden associated with the consumption of these products. A review of the available literature since the passage of the 1996 Food Quality Protection Act was conducted on the barriers to implementing irradiation as a food safety process for fresh fruits and vegetables. The impediments to adopting widespread utilization of irradiation food processing as a food safety measure involve a complex array of legislative, regulatory, industry, and consumer issues. The FDA’s approval process limits the expansion of the list of foods approved for the application of irradiation as a food safety process. There is also a lack of capacity within the industry to meet the needs of a geographically dispersed industry.^

A hybrid method in combining treatment effects from matched and unmatched studies

In the biomedical studies, the general data structures have been the matched (paired) and unmatched designs. Recently, many researchers are interested in Meta-Analysis to obtain a better understanding from several clinical data of a medical treatment. The hybrid design, which is combined two data structures, may create the fundamental question for statistical methods and the challenges for statistical inferences. The applied methods are depending on the underlying distribution. If the outcomes are normally distributed, we would use the classic paired and two independent sample T-tests on the matched and unmatched cases. If not, we can apply Wilcoxon signed rank and rank sum test on each case. ^ To assess an overall treatment effect on a hybrid design, we can apply the inverse variance weight method used in Meta-Analysis. On the nonparametric case, we can use a test statistic which is combined on two Wilcoxon test statistics. However, these two test statistics are not in same scale. We propose the Hybrid Test Statistic based on the Hodges-Lehmann estimates of the treatment effects, which are medians in the same scale.^ To compare the proposed method, we use the classic meta-analysis T-test statistic on the combined the estimates of the treatment effects from two T-test statistics. Theoretically, the efficiency of two unbiased estimators of a parameter is the ratio of their variances. With the concept of Asymptotic Relative Efficiency (ARE) developed by Pitman, we show ARE of the hybrid test statistic relative to classic meta-analysis T-test statistic using the Hodges-Lemann estimators associated with two test statistics.^ From several simulation studies, we calculate the empirical type I error rate and power of the test statistics. The proposed statistic would provide effective tool to evaluate and understand the treatment effect in various public health studies as well as clinical trials.^

Differential Activity of the KRAS Oncogene by Method of Activation: Implications for Signaling and Therapeutic Intervention

Despite having been identified over thirty years ago and definitively established as having a critical role in driving tumor growth and predicting for resistance to therapy, the KRAS oncogene remains a target in cancer for which there is no effective treatment. KRas is activated b y mutations at a few sites, primarily amino acid substitutions at codon 12 which promote a constitutively active state. I have found that different amino acid substitutions at codon 12 can activate different KRas downstream signaling pathways, determine clonogenic growth potential and determine patient response to molecularly targeted therapies. Computer modeling of the KRas structure shows that different amino acids substituted at the codon 12 position influences how KRas interacts with its effecters. In the absence of a direct inhibitor of mutant KRas several agents have recently entered clinical trials alone and in combination directly targeting two of the common downstream effecter pathways of KRas, namely the Mapk pathway and the Akt pathway. These inhibitors were evaluated for efficacy against different KRAS activating mutations. An isogenic panel of colorectal cells with wild type KRas replaced with KRas G12C, G12D, or G12V at the endogenous loci differed in sensitivity to Mek and Akt inhibition. In contrast, screening was performed in a broad panel of lung cell lines alone and no correlation was seen between types of activating KRAS mutation due to concurrent oncogenic lesions. To find a new method to inhibit KRAS driven tumors, siRNA screens were performed in isogenic lines with and without active KRas. The knockdown of CNKSR1 (CNK1) showed selective growth inhibition in cells with an oncogenic KRAS. The deletion of CNK1 reduces expression of mitotic cell cycle proteins and arrests cells with active KRas in the G1 phase of the cell cycle similar to the deletion of an activated KRas regardless of activating substitution. CNK1 has a PH domain responsible for localizing it to membrane lipids making KRas potentially amenable to inhibition with small molecules. The work has identified a series of small molecules capable of binding to this PH domain and inhibiting CNK1 facilitated KRas signaling.