BACKGROUND: Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype. RESULTS: We utilized our previously published Genome Alteration Detection Analysis (GADA) algorithm to identify common ancestry CNVs (caCNVs) and built a caCNV model to predict population structure. We identified a 73 caCNV signature using a training set of 225 healthy individuals from European, Asian, and African ancestry. The signature was validated on an independent test set of 300 individuals with similar ancestral background. The error rate in predicting ancestry in this test set was 2% using the 73 caCNV signature. Among the caCNVs identified, several were previously confirmed experimentally to vary by ancestry. Our signature also contains a caCNV region with a single microRNA (MIR270), which represents the first reported variation of microRNA by ancestry. CONCLUSIONS: We developed a new methodology to identify common CNVs and demonstrated its performance by building a caCNV signature to predict human ancestry with high accuracy. The utility of our approach could be extended to large case-control studies to identify CNV signatures for other phenotypes such as disease susceptibility and drug response.

MOTIVATION: Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values. RESULTS: The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

In this paper, block diagonal linear discriminant analysis (BDLDA) is improved and applied to gene expression data. BDLDA is a classification tool with embedded feature selection, that has demonstrated good performance on simulated data. However, by using cross validation in training, BDLDA is time consuming, thus not an appropriate algorithm for gene expression data, which has a large number of features and relatively small number of samples. In our algorithm, estimated error rate is used as a measure to choose the best model. The algorithm is optimized by repeating the model construction procedure with previously selected features removed, which leads to increased classification robustness. Our algorithm is tested using 10 fold cross validation. In most simulated and real data, our method outperforms the state-of-the-art techniques, showing promise for its use in microarray classification problems. The resulting block structure allows to identify discriminating correlated genes, which is potentially useful in cancer research.

MOTIVATION: The complexity of a large number of recently discovered copy number polymorphisms is much higher than initially thought, thus making it more difficult to detect them in the presence of significant measurement noise. In this scenario, separate normalization and segmentation is prone to lead to many false detections of changes in copy number. New approaches capable of jointly modeling the copy number and the non-copy number (noise) hybridization effects across multiple samples will potentially lead to more accurate results. METHODS: In this article, the genome alteration detection analysis (GADA) approach introduced in our previous work is extended to a multiple sample model. The copy number component is independent for each sample and uses a sparse Bayesian prior, while the reference hybridization level is not necessarily sparse but identical on all samples. The expectation maximization (EM) algorithm used to fit the model iteratively determines whether the observed hybridization levels are more likely due to a copy number variation or to a shared hybridization bias. RESULTS: The new proposed approach is compared with the currently used strategy of separate normalization followed by independent segmentation of each array. Real microarray data obtained from HapMap samples are randomly partitioned to create different reference sets. Using the new approach, copy number and reference intensity estimates are significantly less variable if the reference set changes; and a higher consistency on copy numbers detected within HapMap family trios is obtained. Finally, the running time to fit the model grows linearly in the number samples and probes. AVAILABILITY: http://biron.usc.edu/~piquereg/GADA.

MOTIVATION: Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) that are associated with the development and behavior of tumors. Advances in microarray technology have allowed for greater resolution in detection of DNA copy number changes (amplifications or deletions) across the genome. However, the increase in number of measured signals and accompanying noise from the array probes present a challenge in accurate and fast identification of breakpoints that define CNA. This article proposes a novel detection technique that exploits the use of piece wise constant (PWC) vectors to represent genome copy number and sparse Bayesian learning (SBL) to detect CNA breakpoints. METHODS: First, a compact linear algebra representation for the genome copy number is developed from normalized probe intensities. Second, SBL is applied and optimized to infer locations where copy number changes occur. Third, a backward elimination (BE) procedure is used to rank the inferred breakpoints; and a cut-off point can be efficiently adjusted in this procedure to control for the false discovery rate (FDR). RESULTS: The performance of our algorithm is evaluated using simulated and real genome datasets and compared to other existing techniques. Our approach achieves the highest accuracy and lowest FDR while improving computational speed by several orders of magnitude. The proposed algorithm has been developed into a free standing software application (GADA, Genome Alteration Detection Algorithm). AVAILABILITY: http://biron.usc.edu/~piquereg/GADA

Alterations in the number of DNA copies are very common in tumor cells and may have a very important role in cancer development and progression. New array platforms provide means to analyze the copy number by comparing the hybridization intensities of thousands of DNA sections along the genome. However, detecting and locating the copy number changes from this data is a very challenging task due to the large amount of biological processes that affect hybridization and cannot be controlled. This paper proposes a new technique that exploits the key characteristic that the DNA copy number is piecewise-constant along the genome. First, wavelet footprints are used to obtain a basis for representing the DNA copy number that is maximally sparse in the number of copy number change points. Second, sparse Bayesian learning is applied to infer the copy number changes from noisy array probe intensities. Results demonstrate that sparse Bayesian learning has better performance than matching pursuits methods for this high coherence dictionary. Finally, our results are also shown to be very competitive in performance as compared to state-of-the-art methods for copy number detection.