A survey of analysis software for array-comparative genomic hybridisation studies to detect copy number variation

Implications of germline copy-number variations in psychiatric disorders: review of large-scale genetic studies

Copy number variants (CNVs), defined as genome sequences of ≥50 bp that differ in copy number from that in a reference genome, are a common form of structural variation. Germline CNVs account for some of the missing heritability that single nucleotide polymorphisms could not account for. Recent technological advances have had a huge impact on CNV research. Microarray technology enables relatively low-cost, high-throughput, genome-wide measurements, and short-read sequencing technology enables the detection of short CNVs that cannot be detected by microarrays. As a result, large-scale genetic studies have been able to identify a variety of common and rare germline CNVs and their associations with diseases. Rare germline CNVs have been reported to be associated with neuropsychiatric disorders. In this review, we focused on germline CNVs and briefly described their functional characteristics, formation mechanisms, detection methods, related databases, and the latest findings. Finally, we introduced recent large-scale genetic studies to assess associations of CNVs with diseases, especially psychiatric disorders, and discussed the use of CNV-based animal models to investigate the molecular and cellular mechanisms underlying these disorders. The development and implementation of improved detection methods, such as long-read single-molecule sequencing, are expected to provide additional insight into the molecular basis of psychiatric disorders and other complex diseases, thus facilitating basic and clinical research on CNVs.

Noise-robust assessment of SNP array based CNV calls through local noise estimation of log R ratios

Arrays based on single nucleotide polymorphisms (SNPs) have been successful for the large scale discovery of copy number variants (CNVs). However, current CNV calling algorithms still have limitations in detecting CNVs with high specificity and sensitivity, especially in case of small (<100 kb) CNVs. Therefore, this study presents a simple statistical analysis to evaluate CNV calls from SNP arrays in order to improve the noise-robustness of existing CNV calling algorithms. The proposed approach estimates local noise of log R ratios and returns the probability that a certain observation is different from this log R ratio noise level. This probability can be triggered at different thresholds to tailor specificity and/or sensitivity in a flexible way. Moreover, a comparison based on qPCR experiments showed that the proposed noise-robust CNV calls outperformed original ones for multiple threshold values.

Comprehensive Analysis of Genome Rearrangements in Eight Human Malignant Tumor Tissues

Carcinogenesis is a complex multifactorial, multistage process, but the precise mechanisms are not well understood. In this study, we performed a genome-wide analysis of the copy number variation (CNV), breakpoint region (BPR) and fragile sites in 2,737 tumor samples from eight tumor entities and in 432 normal samples. CNV detection and BPR identification revealed that BPRs tended to accumulate in specific genomic regions in tumor samples whereas being dispersed genome-wide in the normal samples. Hotspots were observed, at which segments with similar alteration in copy number were overlapped along with BPRs adjacently clustered. Evaluation of BPR occurrence frequency showed that at least one was detected in about and more than 15% of samples for each tumor entity while BPRs were maximal in 12% of the normal samples. 127 of 2,716 tumor-relevant BPRs (termed 'common BPRs') exhibited also a noticeable occurrence frequency in the normal samples. Colocalization assessment identified 20,077 CNV-affecting genes and 169 of these being known tumor-related genes. The most noteworthy genes are KIAA0513 important for immunologic, synaptic and apoptotic signal pathways, intergenic non-coding RNA RP11-115C21.2 possibly acting as oncogene or tumor suppressor by changing the structure of chromatin, and ADAM32 likely importance in cancer cell proliferation and progression by ectodomain-shedding of diverse growth factors, and the well-known tumor suppressor gene p53. The BPR distributions indicate that CNV mutations are likely non-random in tumor genomes. The marked recurrence of BPRs at specific regions supports common progression mechanisms in tumors. The presence of hotspots together with common BPRs, despite its small group size, imply a relation between fragile sites and cancer-gene alteration. Our data further suggest that both protein-coding and non-coding genes possessing a range of biological functions might play a causative or functional role in tumor biology. This research enhances our understanding of the mechanisms for tumorigenesis and progression.

Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays

Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

Copy number variation signature to predict human ancestry

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.