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.     

Quantitative proteomic analysis of ribosomal protein L35b mutant of Saccharomyces cerevisiae

Recent studies have revealed that in higher eukaryotes, several ribosomal proteins are involved in some pathological events or developmental defects, indicating that ribosomal proteins perform unconventional functions other than protein biosynthesis. To obtain an insight into the novel roles of ribosomal proteins, we aimed to analyze the changes in proteome expression in ribosomal protein mutants by using Saccharomyces cerevisiae as a model system. We introduced the rpl35bΔ mutation into the 4159 green fluorescent protein (GFP)-tagged yeast strains by using the synthetic genetic array (SGA) method, and performed quantitative proteomic analysis by using a multilabel microplate reader and flow cytometer. We identified 22 upregulated and 20 downregulated proteins in the rpl35bΔ mutant. These proteins were primarily classified into the Gene Ontology (GO) categories of cellular biosynthetic process, translation, protein or nucleotide metabolic process, cell wall organization and biogenesis, and hyperosmotic response. We also investigated the correlation between the mRNA and protein levels of the identified proteins. Our results show that a ribosomal protein mutation can lead to perturbation in the expression of several proteins, including some other ribosomal proteins. Furthermore, our approach of combining a library of GFP-tagged yeast strains and the SGA method provides an effective and highly sensitive method for dynamic analysis of the effects of various mutations on proteome expression.     

Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information

Background

Whole genome sequencing of bisulfite converted DNA (‘methylC-seq’) method provides comprehensive information of DNA methylation. An important application of these whole genome methylation maps is classifying each position as a methylated versus non-methylated nucleotide. A widely used current method for this purpose, the so-called binomial method, is intuitive and straightforward, but lacks power when the sequence coverage and the genome-wide methylation level are low. These problems present a particular challenge when analyzing sparsely methylated genomes, such as those of many invertebrates and plants.

Results

We demonstrate that the number of sequence reads per position from methylC-seq data displays a large variance and can be modeled as a shifted negative binomial distribution. We also show that DNA methylation levels of adjacent CpG sites are correlated, and this similarity in local DNA methylation levels extends several kilobases. Taking these observations into account, we propose a new method based on Bayesian classification to infer DNA methylation status while considering the neighborhood DNA methylation levels of a specific site. We show that our approach has higher sensitivity and better classification performance than the binomial method via multiple analyses, including computational simulations, Area Under Curve (AUC) analyses, and improved consistencies across biological replicates. This method is especially advantageous in the analyses of sparsely methylated genomes with low coverage.

Conclusions

Our method improves the existing binomial method for binary methylation calls by utilizing a posterior odds framework and incorporating local methylation information. This method should be widely applicable to the analyses of methylC-seq data from diverse sparsely methylated genomes. Bis-Class and example data are provided at a dedicated website (http://bibs.snu.ac.kr/software/Bisclass).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-608) contains supplementary material, which is available to authorized users.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

Analysis of human disease genes in the context of gene essentiality

The characteristics of human disease genes were investigated through a comparative analysis with mouse mutant phenotype data. Mouse orthologs with mutations that resulted in discernible phenotypes were separated from mutations with no phenotypic defect, listing ‘phenotype’ and ‘no phenotype’ genes. First, we showed that phenotype genes are more likely to be disease genes compared to no phenotype genes. Phenotype genes were further divided into ‘embryonic lethal’, ‘postnatal lethal’, and ‘non-lethal phenotype’ groups. Interestingly, embryonic lethal genes, the most essential genes in mouse, were less likely to be disease genes than postnatal lethal genes. These findings indicate that some extremely essential genes are less likely to be disease genes, although human disease genes tend to display characteristics of essential genes. We also showed that, in lethal groups, non-disease genes tend to evolve slower than disease genes indicating a strong purifying selection on non-disease genes in this group. In addition, phenotype and no phenotype groups showed differing types of disease mutations. Disease genes in the no phenotype group displayed a higher frequency of regulatory mutations while those in the phenotype group had more frequent coding mutations, indicating that the types of disease mutations vary depending on gene essentiality. Furthermore, missense disease mutations in no phenotype genes were found to be more radical amino acid substitutions than those in phenotype genes.     

Analysis of significant protein abundance from multiple reaction-monitoring data

Background

Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs. However, LMMs often provide various significance results, depending on model specification. Sometimes it would be difficult to specify a correct LMM method for the analysis of MRM data. Here, we propose a new logistic regression-based method for Significance Analysis of Multiple Reaction Monitoring (LR-SAM).

Results

Through simulation studies, we demonstrate that LMM methods may not preserve type I error, thus yielding high false- positive errors, depending on how random effects are specified. Our simulation study also shows that the LR-SAM approach performs similarly well as LMM approaches, in most cases. However, LR-SAM performs better than the LMMs, particularly when the effects sizes of peptides from the same protein are heterogeneous. Our proposed method was applied to MRM data for identification of proteins associated with clinical responses of treatment of 115 hepatocellular carcinoma (HCC) patients with the tyrosine kinase inhibitor sorafenib. Of 124 candidate proteins, LMM approaches provided 6 results varying in significance, while LR-SAM, by contrast, yielded 18 significant results that were quite reproducibly consistent.

Conclusion

As exemplified by an application to HCC data set, LR-SAM more effectively identified proteins associated with clinical responses of treatment than LMM did.