A Modified Entropy-Based Approach for Identifying Gene-Gene Interactions in Case-Control Study

Gene-gene interactions may play an important role in the genetics of a complex disease. Detection and characterization of gene-gene interactions is a challenging issue that has stimulated the development of various statistical methods to address it. In this study, we introduce a method to measure gene interactions using entropy-based statistics from a contingency table of trait and genotype combinations. We also developed an exploration procedure by using graphs. We propose a standardized relative information gain (RIG) measure to evaluate the interactions between single nucleotide polymorphism (SNP) combinations. To identify the k ^th order interactions, contingency tables of trait and genotype combinations of k SNPs are constructed, with which RIGs are calculated. The RIGs are standardized using the mean and standard deviation from the permuted datasets. SNP combinations yielding high standardized RIG are chosen for gene-gene interactions. Detection of high-order interactions and comparison of interaction strengths between different orders are made possible by using standardized RIG. We have applied the proposed standardized entropy-based method to two types of data sets from a simulation study and a real genetic association study. We have compared our method and the multifactor dimensionality reduction (MDR) method through power analysis of eight different genetic models with varying penetrance rates, number of SNPs, and sample sizes. Our method shows successful identification of genetic associations and gene-gene interactions both in simulation and real genetic data. Simulation results suggest that the proposed entropy-based method is better able to detect high-order interactions and is superior to the MDR method in most cases. The proposed method is well suited for detecting interactions without main effects as well as for models including main effects.     

arrayQCplot: software for checking the quality of microarray data

SUMMARY: arrayQCplot is a software for the exploratory analysis of microarray data. This software focuses on quality control and generates newly developed plots for quality and reproducibility checks. It is developed using R and provides a user-friendly graphical interface for graphics and statistical analysis. Therefore, novice users will find arrayQCplot as an easy-to-use software for checking the quality of their data by a simple mouse click. AVAILABILITY: arrayQCplot software is available from Bioconductor at http://www.bioconductor.org. A more detailed manual is available at http://bibs.snu.ac.kr/software/arrayQCplot CONTACT: tspark@stats.snu.ac.kr.     

Log-linear model-based multifactor dimensionality reduction method to detect gene gene interactions

MOTIVATION: The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene-gene interaction) and environmental factors (gene-environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene-gene interactions or gene-environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined. RESULTS: In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.     

Synthetic biology for biofuels: Building designer microbes from the scratch

The ultimate goal in the production of biofuels is to produce fuels identical or similar to petroleum-derived transportation fuels more efficiently and in commercial quantities. Synthetic biologists have been engineering microbes to synthesize biofuels, such as butanol and fatty acid- or isoprenoid-based fuels, which are nearly identical to gasoline and diesel. One of the most urgent demands along this direction is to attain a solid framework for characterizing and standardizing the biological parts and devices. It seems quite promising because biotechnologies specially based on miniaturizations have been making a big contribution to this work. Therefore, in this review, recent advances and difficulties in the biofuel field are discussed, along with the advances of synthetic biology, which will make it possible to create designer microorganisms that produce economically viable next generation biofuels, aside from bioethanol, from corn or sugar cane, and biodiesel from plant or animal oils.     

Error-pooling-based statistical methods for identifying novel temporal replication profiles of human chromosomes observed by DNA tiling arrays

Statistical analysis on tiling array data is extremely challenging due to the astronomically large number of sequence probes, high noise levels of individual probes and limited number of replicates in these data. To overcome these difficulties, we first developed statistical error estimation and weighted ANOVA modeling approaches to high-density tiling array data, especially the former based on an advanced error-pooling method to accurately obtain heterogeneous technical error of small-sample tiling array data. Based on these approaches, we analyzed the high-density tiling array data of the temporal replication patterns during cell-cycle S phase of synchronized HeLa cells on human chromosomes 21 and 22. We found many novel temporal replication patterns, identifying about 26% of over 1 million tiling array sequence probes with significant differential replication during the four 2-h time periods of S phase. Among these differentially replicated probes, 126941 sequence probes were matched to 417 known genes. The majority of these genes were found to be replicated within one or two consecutive time periods, while the others were replicated at two non-consecutive time periods. Also, coding regions found to be more differentially replicated in particular time periods than noncoding regions in the gene-poor chromosome 21 (25% differentially replicated among genic probes versus 18.6% among intergenic probes), while such a phenomenon was less prominent in gene-rich chromosome 22. A rigorous statistical testing for local proximity of differentially replicated genic and intergenic probes was performed to identify significant stretches of differentially replicated sequence regions. From this analysis, we found that adjacent genes were frequently replicated at different time periods, potentially implying the existence of quite dense replication origins. Evaluating the conditional probability significance of identified gene ontology terms on chromosomes 21 and 22, we detected some over-represented molecular functions and biological processes among these differentially replicated genes, such as the ones relevant to hydrolase, transferase and receptor-binding activities. Some of these results were confirmed showing >70% consistency with cDNA microarray data that were independently generated in parallel with the tiling arrays. Thus, our improved analysis approaches specifically designed for high-density tiling array data enabled us to reliably and sensitively identify many novel temporal replication patterns on human chromosomes.     

Molecular and ecological analyses of microbial community structures in biofilms of a full-scale Aerated Up-Flow Biobead process

Molecular and cultivation techniques were used to characterize the bacterial communities of biobead reactor biofilms in a sewage treatment plant to which an Aerated Up-Flow Biobead process was applied. With this biobead process, the monthly average values of various chemical parameters in the effluent were generally kept under the regulation limits of the effluent quality of the sewage treatment plant during the operation period. Most probable number (MPN) analysis revealed that the population of denitrifying bacteria was abundant in the biobead #1 reactor, denitrifying and nitrifying bacteria coexisted in the biobead #2 reactor, and nitrifying bacteria prevailed over denitrifying bacteria in the biobead #3 reactor. The results of the MPN test suggested that the biobead #2 reactor was a transition zone leading to acclimated nitrifying biofilms in the biobead #3 reactor. Phylogenetic analysis of 16S rDNA sequences cloned from biofilms showed that the biobead #1 reactor, which received a high organic loading rate, had much diverse microorganisms, whereas the biobead #2 and #3 reactors were dominated by the members of Proteobacteria. DGGE analysis with the ammonia monooxygenase (amoA) gene supported the observation from the MPN test that the biofilms of September were fully developed and specialized for nitrification in the biobead reactor #3. All of the DNA sequences of the amoA DGGE bands were very similar to the sequence of the amoA gene of Nitrosomonas species, the presence of which is typical in the biological aerated filters. The results of this study showed that organic and inorganic nutrients were efficiently removed by both denitrifying microbial populations in the anaerobic tank and heterotrophic and nitrifying bacterial biofilms well-formed in the three functional biobead reactors in the Aerated Up-Flow Biobead process.     

Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions

MOTIVATION: The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk. RESULTS: While the original MDR method provides a simple binary measure of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group. AVAILABILITY: The program written in R is available.     

Large-scale genome-wide association study of Asian population reveals genetic factors in FRMD4A and other loci influencing smoking initiation and nicotine dependence

Diseases related to smoking are the second leading cause of death in the world. Cigarette smoking is a risk factor for several diseases such as cancer and cardiovascular and respiratory disorders. Despite increasing evidence of genetic determination, the susceptibility genes and loci underlying various aspects of smoking behavior are largely unknown. Moreover, almost all reported genome-wide association studies (GWASs) have been performed on samples of European origin, limiting the applicability of the results to other ethnic populations. In this first GWAS on smoking behavior in an Asian population, after analyzing 8,842 DNA samples from the Korea Association Resource project with 352,228 single nucleotide polymorphisms (SNPs) genotyped for each sample, we identified 8 SNPs significantly associated with smoking initiation (SI) and 4 with nicotine dependence (ND). Because of the current unavailability of an independent Asian smoking sample, we replicated the discoveries in independent samples of European-American and African-American origin. Of the 12 SNPs examined in the replicated samples, we identified two SNPs, in the regulator of G-protein signaling 17 gene (rs7747583, p value(meta)?=?6.40?×?10(-6); rs2349433, p value(meta)?=?5.57?×?10(-6)), associated with SI. Also, we found two SNPs significantly associated with ND; one in the FERM domain containing 4A (rs4424567, p value(meta)?=?2.30?×?10(-6)) and the other at 7q31.1 (rs848353, p value(meta)?=?9.16?×?10(-8)). These SNPs represent novel targets for examination of smoking behavior and warrant further investigation using independent samples.