An accurate and powerful method for copy number variation detection.

MOTIVATION: Integration of multiple genetic sources for copy number variation detection (CNV) is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change point based methods can increase statistical power to identify variants, it remains challenging to effectively detect CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. To boost statistical power for the identification of variants, here we present a novel improvement that integrates the relative allelic intensity with external information from empirical statistics with modeling, which we called modSaRa2. RESULTS: Simulation studies illustrated that modSaRa2 markedly improved both sensitivity and specificity over existing methods for analyzing array-based data. The improvement in weak CNV signal detection is the most substantial, while it also simultaneously improves stability when CNV size varies. The application of the new method to a whole genome melanoma dataset identified novel candidate melanoma risk associated deletions on chromosome bands 1p22.2 and duplications on 6p22, 6q25 and 19p13 regions, which may facilitate the understanding of the possible roles of germline copy number variants in the etiology of melanoma. AVAILABILITY AND IMPLEMENTATION: http://c2s2.yale.edu/software/modSaRa2 or https://github.com/FeifeiXiaoUSC/modSaRa2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Regression and data mining methods for analyses of multiple rare variants in the Genetic Analysis Workshop 17 mini-exome data.

Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors.

NONPARAMETRIC ESTIMATION OF GENEWISE VARIANCE FOR MICROARRAY DATA.

Estimation of genewise variance arises from two important applications in microarray data analysis: selecting significantly differentially expressed genes and validation tests for normalization of microarray data. We approach the problem by introducing a two-way nonparametric model, which is an extension of the famous Neyman-Scott model and is applicable beyond microarray data. The problem itself poses interesting challenges because the number of nuisance parameters is proportional to the sample size and it is not obvious how the variance function can be estimated when measurements are correlated. In such a high-dimensional nonparametric problem, we proposed two novel nonparametric estimators for genewise variance function and semiparametric estimators for measurement correlation, via solving a system of nonlinear equations. Their asymptotic normality is established. The finite sample property is demonstrated by simulation studies. The estimators also improve the power of the tests for detecting statistically differentially expressed genes. The methodology is illustrated by the data from MicroArray Quality Control (MAQC) project.

Metabolic Analysis Reveals Altered Long-Chain Fatty Acid Metabolism in the Host by Huanglongbing Disease.

Candidatus Liberibacter asiaticus (CLas) is the presumed causal agent of Huanglongbing, one of the most destructive diseases in citrus. However, the lipid metabolism component of host response to this pathogen has not been investigated well. Here, metabolic profiling of a variety of long-chain fatty acids and their oxidation products was first performed to elucidate altered host metabolic responses of disease. Fatty acid signals were found to decrease obviously in response to disease regardless of cultivar. Several lipid oxidation products strongly correlated with those fatty acids were also consistently reduced in the diseased group. Using a series of statistical methods and metabolic pathway mapping, we found significant markers contributing to the pathological symptoms and identified their internal relationships and metabolic network. Our findings suggest that the infection of CLas may cause the altered metabolism of long-chain fatty acids, possibly leading to manipulation of the host's defense derived from fatty acids.

Lipidomic analysis for carbonyl species derived from fish oil using liquid chromatography-tandem mass spectrometry.

Lipid peroxidation gives rise to carbonyl species, some of which are reactive and play a role in the pathogenesis of numerous human diseases. Oils are ubiquitous sources that can be easily oxidized to generate these compounds under oxidative stress. In this present work, we developed a targeted lipidomic method for the simultaneous determination of thirty-five aldehydes and ketones derived from fish oil, the omega-3 fatty acid-rich source, by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The analytes include highly toxic reactive carbonyl species (RCS) such as acrolein, crotonaldehyde, trans-4-hydroxy-2-hexenal (HHE), trans-4-hydroxy-2-nonenal (HNE), trans-4-oxo-2-nonenal (ONE), glyoxal and methylglyoxal, all of which are promising biomarkers of lipid peroxidation. They were formed using in vitro Fe(II)-mediated oxidation, and derivatized using 2,4-dinitrophenylhydrazine (DNPH) for the feasibility of quantitative assay. Before analysis, solid phase extraction (SPE) was used to clean samples further. Uniquely different patterns of carbonyl compound generation between omega-3 and 6 fatty acids were observed using this lipidomic approach. The method developed was both validated, and successfully applied to monitor formation of carbonyl species by lipid peroxidation using ten different fish oil products. Hypotheses of correlations between the monitored dataset of analytes and their parent fatty acids were also tested using the Pearson's correlation test. Results indicate our method is a useful analytical tool for lipid peroxidation studies.

THE SCREENING AND RANKING ALGORITHM TO DETECT DNA COPY NUMBER VARIATIONS.

DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect CNVs based on data that generated by genome analysis platforms. However, most algorithms are computationally intensive with complexity at least O(n2), where n is the number of probes in the experiments. Moreover, the theoretical properties of those existing methods are not well understood. A faster and better characterized algorithm is desirable for the ultra high throughput data. In this study, we propose the Screening and Ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.

Detection of rare functional variants using group ISIS.

Genome-wide association studies have been firmly established in investigations of the associations between common genetic variants and complex traits or diseases. However, a large portion of complex traits and diseases cannot be explained well by common variants. Detecting rare functional variants becomes a trend and a necessity. Because rare variants have such a small minor allele frequency (e.g., <0.05), detecting functional rare variants is challenging. Group iterative sure independence screening (ISIS), a fast group selection tool, was developed to select important genes and the single-nucleotide polymorphisms within. The performance of the group ISIS and group penalization methods is compared for detecting important genes in the Genetic Analysis Workshop 17 data. The results suggest that the group ISIS is an efficient tool to discover genes and single-nucleotide polymorphisms associated to phenotypes.